
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from scipy import stats
%matplotlib inline
import warnings
warnings.filterwarnings("ignore")


signal_df = pd.read_csv('signal-data.csv')


signal_df.head()


signal_df.shape

(1567, 592)


signal_df.columns

Index(['Time', '0', '1', '2', '3', '4', '5', '6', '7', '8',
       ...
       '581', '582', '583', '584', '585', '586', '587', '588', '589',
       'Pass/Fail'],
      dtype='object', length=592)


signal_df.dtypes

Time          object
0            float64
1            float64
2            float64
3            float64
              ...   
586          float64
587          float64
588          float64
589          float64
Pass/Fail      int64
Length: 592, dtype: object


signal_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1567 entries, 0 to 1566
Columns: 592 entries, Time to Pass/Fail
dtypes: float64(590), int64(1), object(1)
memory usage: 7.1+ MB


signal_df.describe()


signal_df.isna().sum()

Time          0
0             6
1             7
2            14
3            14
             ..
586           1
587           1
588           1
589           1
Pass/Fail     0
Length: 592, dtype: int64


signal_df['Pass/Fail'].unique()

array([-1,  1], dtype=int64)


signal_df['Pass/Fail'].isna().sum()

0


signal_df.describe().T.style


signal_df.describe().style


signal_df['Pass/Fail'].value_counts()

-1    1463
 1     104
Name: Pass/Fail, dtype: int64


signal_df.isnull().sum().sum()

41951


signal_df = signal_df.drop('Time', axis=1)
for col in signal_df.columns:
    if signal_df[col].isnull().sum() / len(signal_df) >= 0.2:
        print(col)
        signal_df.drop(col, axis=1, inplace=True)
    else:
        signal_df[col].fillna(signal_df[col].mean(), inplace=True)

72
73
85
109
110
111
112
157
158
220
244
245
246
247
292
293
345
346
358
382
383
384
385
492
516
517
518
519
578
579
580
581


signal_df.isnull().sum().sum()

0


signal_df.shape

(1567, 559)


# Here we are using the nunique fucntion to identify the number of unique values in the columns.
#The columns which has only one Unique value is being dropped
nunique = signal_df.nunique()
cols_to_drop = nunique[nunique == 1].index
signal_df.drop(cols_to_drop, axis=1,inplace=True)
print(signal_df.shape)

(1567, 443)


signal_var = signal_df.drop('Pass/Fail', 1).var().round(2)
low_var_features = signal_var[signal_var <= 0.1]
low_var_features

7      0.00
8      0.01
9      0.00
10     0.00
11     0.00
       ... 
583    0.00
584    0.00
586    0.00
587    0.00
588    0.00
Length: 188, dtype: float64


signal_lvr = signal_df.drop(np.unique(low_var_features.index),axis=1)
signal_lvr.shape

(1567, 255)


signal_lvr.head(10)


sns.heatmap(signal_lvr.corr(), center=0)

<Axes: >


corel_mat=signal_lvr.corr()
corel_mat.head(10)


upper = corel_mat.where(np.triu(np.ones(corel_mat.shape), k=1).astype(np.bool))
upper.head(10)


hi_col=[column for column in upper.columns if any(upper[column] >.9)]
print("Columns having corelation greater than .9 which is industry standard",len(hi_col))

Columns having corelation greater than .9 which is industry standard 95


signal_new = signal_lvr.drop(columns = hi_col)
signal_new.shape

(1567, 160)


signal_new.head(10)


#finding unique values in each columns
df = signal_new.drop('Pass/Fail', 1).nunique()
#finding columns having less than 10 unique values

Drop = df[df <= 10]
print("Features having less than 10 unique values \n", Drop)

Features having less than 10 unique values 
 209    3
521    9
dtype: int64


signal_new=signal_new.drop(np.unique(Drop.index),axis=1)


signal_new.shape

(1567, 158)


columns = list(signal_new.iloc[:,1:158]) 
signal_new[columns].hist(stacked=True, bins=100, figsize=(18,30), layout=(20,10));


sns.countplot(data= signal_new, x= signal_new['Pass/Fail'])

<Axes: xlabel='Pass/Fail', ylabel='count'>


signal_new['Pass/Fail'].value_counts(normalize=True)

-1    0.933631
 1    0.066369
Name: Pass/Fail, dtype: float64


plt.figure(figsize=(20, 50))
col = 1
for i in signal_new.columns:
    plt.subplot(56,10, col)
    sns.boxplot(signal_new[i],color='blue',orient='h')
    col += 1


sns.pairplot(signal_new.iloc[:,0:15]);


sns.pairplot(signal_new.iloc[:,16:30]);


plt.rcParams['figure.figsize'] = (18, 18)
sns.heatmap(signal_new.corr(), cmap = "viridis")
plt.title('Correlation heatmap for the Data', fontsize = 20)

Text(0.5, 1.0, 'Correlation heatmap for the Data')


signal_new.columns

Index(['0', '1', '2', '3', '4', '6', '12', '14', '15', '16',
       ...
       '561', '562', '564', '569', '570', '572', '577', '585', '589',
       'Pass/Fail'],
      dtype='object', length=158)


X = signal_new.drop('Pass/Fail', axis=1)
y = signal_new['Pass/Fail']


X.head()


y.head()

0   -1
1   -1
2    1
3   -1
4   -1
Name: Pass/Fail, dtype: int64


y.value_counts(normalize=True)

-1    0.933631
 1    0.066369
Name: Pass/Fail, dtype: float64


from imblearn.over_sampling import SMOTE

smote= SMOTE()
X_os, y_os= smote.fit_resample(X, y)


print(X.shape)
print(y.shape)
print(X_os.shape)
print(y_os.shape)

(1567, 157)
(1567,)
(2926, 157)
(2926,)


y_os.value_counts()

-1    1463
 1    1463
Name: Pass/Fail, dtype: int64


from sklearn.model_selection import train_test_split

x_train, x_test, y_train, y_test = train_test_split(X_os, y_os, test_size=0.25, stratify = y_os, random_state=101)


print("x_train shape",x_train.shape)
print("x_test shape",x_test.shape)
print("y_train shape",y_train.shape)
print("y_test shape",y_test.shape)

x_train shape (2194, 157)
x_test shape (732, 157)
y_train shape (2194,)
y_test shape (732,)


from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()

x_train = scaler.fit_transform(x_train)
x_test = scaler.transform(x_test)


col=X.columns


x_train

array([[-0.14529223, -0.65771275,  0.64506089, ..., -0.56907992,
        -0.1944015 ,  0.52811401],
       [-1.62119751,  0.3236986 , -1.34099509, ..., -0.67282274,
        -0.87604016,  1.24647417],
       [ 0.50428457, -1.11702265,  0.11176889, ..., -0.06269127,
        -0.94149319, -0.86634674],
       ...,
       [ 0.08698154,  0.53864555,  0.65602362, ..., -0.33184925,
        -0.98685128, -1.20044503],
       [-0.14228397,  2.84989909, -0.85809845, ...,  0.63067931,
        -0.44228299,  2.01506081],
       [ 1.14568173,  0.6517258 , -2.3284581 , ...,  0.43199958,
        -1.00011731, -0.47705725]])


x_test

array([[ 0.32398039, -0.35310065,  0.27792146, ..., -0.13705271,
         0.86628221, -0.76533208],
       [-0.07458028,  0.1648313 ,  0.79132291, ..., -0.26216665,
        -0.86950732, -0.4269443 ],
       [-1.72660119,  0.36235153,  1.94870764, ..., -0.22734821,
         0.09258193, -0.72931933],
       ...,
       [-0.53128696,  0.44024197,  1.3160396 , ...,  0.12547964,
         1.03094982, -0.70503283],
       [ 0.43179597, -0.64932608,  1.69539635, ..., -0.22910387,
        -0.87155394,  0.18346181],
       [-0.13388346, -0.52787645,  0.42006061, ..., -0.26634893,
        -0.23064968,  0.10536855]])


from sklearn.tree import DecisionTreeClassifier
dTree = DecisionTreeClassifier(criterion = 'gini', random_state=101)
from sklearn.metrics import accuracy_score, fbeta_score, make_scorer


dTree.fit(x_train, y_train)

DecisionTreeClassifier(random_state=101)

DecisionTreeClassifier(random_state=101)


print("Train score",dTree.score(x_train,y_train))
print("Test score",dTree.score(x_test,y_test))

Train score 1.0
Test score 0.8797814207650273


from sklearn.model_selection import GridSearchCV, RandomizedSearchCV,KFold, cross_val_score


parameters = {'max_depth': range(1, 20),'criterion': ['gini', 'entropy'],'min_samples_leaf':range(2, 10)}
scorer = make_scorer(fbeta_score, beta=0.5)


dt_classifier= GridSearchCV(dTree,param_grid=parameters,scoring=scorer)
grid_fit = dt_classifier.fit(x_train, y_train)


best_clf = grid_fit.best_estimator_


best_predictions = best_clf.predict(x_test)


dt_gs_result=pd.DataFrame(grid_fit.cv_results_)


dt_gs_result


dt_gs_result[['param_criterion','param_max_depth','params','mean_test_score','rank_test_score']]


# Evaluate the optimized model
print("Optimized Model:")
print("Best parameters:", grid_fit.best_params_)
print("Final accuracy score on testing data: {:.4f}".format(accuracy_score(y_test, best_predictions)))
print("Final F-score on testing data: {:.4f}".format(fbeta_score(y_test, best_predictions, beta=0.5)))

Optimized Model:
Best parameters: {'criterion': 'gini', 'max_depth': 17, 'min_samples_leaf': 2}
Final accuracy score on testing data: 0.8743
Final F-score on testing data: 0.8649


dt_classifier= RandomizedSearchCV(dTree,param_distributions=parameters,scoring=scorer)
random_fit = dt_classifier.fit(x_train, y_train)


dt_Rs_result=pd.DataFrame(random_fit.cv_results_)


dt_Rs_result


dt_Rs_result[['param_max_depth','params','params','rank_test_score','mean_test_score']]


print(random_fit.best_estimator_)
print('\n')
print(random_fit.get_params)
print('\n')
print(random_fit.best_score_)

DecisionTreeClassifier(max_depth=19, min_samples_leaf=3, random_state=101)


<bound method BaseEstimator.get_params of RandomizedSearchCV(estimator=DecisionTreeClassifier(random_state=101),
                   param_distributions={'criterion': ['gini', 'entropy'],
                                        'max_depth': range(1, 20),
                                        'min_samples_leaf': range(2, 10)},
                   scoring=make_scorer(fbeta_score, beta=0.5))>


0.858820091781461


from sklearn.tree import DecisionTreeClassifier
dTree_Gs_bP = DecisionTreeClassifier(criterion = 'gini', max_depth=19,min_samples_leaf=2)


dTree_Gs_bP.fit(x_train, y_train)

DecisionTreeClassifier(max_depth=19, min_samples_leaf=2)

DecisionTreeClassifier(max_depth=19, min_samples_leaf=2)


print("Train score",dTree_Gs_bP.score(x_train,y_train))
print("Test score",dTree_Gs_bP.score(x_test,y_test))

Train score 0.9908842297174111
Test score 0.8702185792349727


dTree_rs_bP = DecisionTreeClassifier(criterion = 'gini', max_depth=11,min_samples_leaf=5,random_state=101)


dTree_rs_bP.fit(x_train, y_train)

DecisionTreeClassifier(max_depth=11, min_samples_leaf=5, random_state=101)

DecisionTreeClassifier(max_depth=11, min_samples_leaf=5, random_state=101)


print("Train score",dTree_rs_bP.score(x_train,y_train))
print("Test score",dTree_rs_bP.score(x_test,y_test))

Train score 0.9685505925250684
Test score 0.8592896174863388


from sklearn.decomposition import PCA
pca=PCA()


pca.fit(x_train)

PCA()

PCA()


print("\nPercentage of variation explained by each eigen Vector\n",pca.explained_variance_ratio_)
print("\n Cumulative Variance Explained\n",np.cumsum(pca.explained_variance_ratio_))

Percentage of variation explained by each eigen Vector
 [6.11427533e-02 5.47323240e-02 3.72684688e-02 3.41549409e-02
 2.93779944e-02 2.60567719e-02 2.31273499e-02 2.09534246e-02
 1.92905770e-02 1.77060911e-02 1.63519053e-02 1.60139245e-02
 1.53352485e-02 1.52376893e-02 1.48552854e-02 1.47570501e-02
 1.41876979e-02 1.36688074e-02 1.34899460e-02 1.30907104e-02
 1.25244629e-02 1.18818901e-02 1.13981361e-02 1.11311322e-02
 1.09433411e-02 1.07765798e-02 1.05494680e-02 1.02908850e-02
 1.00312947e-02 9.98417348e-03 9.62885657e-03 9.45975021e-03
 9.34281143e-03 8.92632873e-03 8.61157630e-03 8.49134486e-03
 8.33887815e-03 8.22624946e-03 7.97175369e-03 7.91111745e-03
 7.68768467e-03 7.49103933e-03 7.41748502e-03 7.14674882e-03
 7.13024133e-03 6.98082591e-03 6.81926957e-03 6.68853940e-03
 6.59767380e-03 6.47299742e-03 6.24119294e-03 6.16529170e-03
 6.06738497e-03 5.93388565e-03 5.81132423e-03 5.75017364e-03
 5.67451062e-03 5.63036151e-03 5.55294881e-03 5.48101990e-03
 5.37705062e-03 5.34321760e-03 5.17453248e-03 5.09449610e-03
 5.04215734e-03 4.94820700e-03 4.83930133e-03 4.76592962e-03
 4.75069227e-03 4.66623920e-03 4.61924729e-03 4.42149596e-03
 4.35366710e-03 4.31260460e-03 4.30101798e-03 4.22670136e-03
 4.16734467e-03 4.06984834e-03 3.98321512e-03 3.89991424e-03
 3.81931581e-03 3.72481939e-03 3.56525333e-03 3.46227934e-03
 3.40524215e-03 3.33770902e-03 3.29342826e-03 3.24316593e-03
 3.21979720e-03 3.15864532e-03 3.08070667e-03 2.98049513e-03
 2.94547701e-03 2.84732652e-03 2.79828710e-03 2.72320382e-03
 2.68235637e-03 2.62609770e-03 2.57192993e-03 2.47762689e-03
 2.40529598e-03 2.38004646e-03 2.35373687e-03 2.29144476e-03
 2.12152724e-03 2.02046085e-03 1.97786044e-03 1.88937336e-03
 1.80470990e-03 1.71094723e-03 1.63287124e-03 1.53626237e-03
 1.51618925e-03 1.47346137e-03 1.45553340e-03 1.42720369e-03
 1.31491427e-03 1.28286615e-03 1.25639791e-03 1.18520129e-03
 1.17068851e-03 1.10845193e-03 1.01532753e-03 9.73307745e-04
 9.44131285e-04 7.78311860e-04 7.50182973e-04 7.20667736e-04
 6.85591312e-04 6.60873836e-04 6.19061767e-04 5.25089242e-04
 4.88373774e-04 4.74799088e-04 4.37882914e-04 4.21418527e-04
 3.40964001e-04 3.29793641e-04 3.15308940e-04 2.85187580e-04
 2.51420251e-04 2.35142673e-04 1.93530410e-04 1.75326261e-04
 1.58478881e-04 1.47733536e-04 1.29004444e-04 1.23122742e-04
 8.32517717e-05 5.80798873e-05 3.68342563e-05 2.71860295e-05
 1.06940680e-05 3.72555383e-06 2.99242644e-06 2.12508291e-06
 1.27036229e-12]

 Cumulative Variance Explained
 [0.06114275 0.11587508 0.15314355 0.18729849 0.21667648 0.24273325
 0.2658606  0.28681403 0.3061046  0.3238107  0.3401626  0.35617653
 0.37151177 0.38674946 0.40160475 0.4163618  0.4305495  0.4442183
 0.45770825 0.47079896 0.48332342 0.49520531 0.50660345 0.51773458
 0.52867792 0.5394545  0.55000397 0.56029486 0.57032615 0.58031032
 0.58993918 0.59939893 0.60874174 0.61766807 0.62627965 0.63477099
 0.64310987 0.65133612 0.65930787 0.66721899 0.67490668 0.68239772
 0.6898152  0.69696195 0.70409219 0.71107302 0.71789229 0.72458083
 0.7311785  0.7376515  0.74389269 0.75005798 0.75612537 0.76205925
 0.76787058 0.77362075 0.77929526 0.78492562 0.79047857 0.79595959
 0.80133664 0.80667986 0.81185439 0.81694889 0.82199104 0.82693925
 0.83177855 0.83654448 0.84129517 0.84596141 0.85058066 0.85500216
 0.85935582 0.86366843 0.86796945 0.87219615 0.87636349 0.88043334
 0.88441656 0.88831647 0.89213579 0.89586061 0.89942586 0.90288814
 0.90629338 0.90963109 0.91292452 0.91616768 0.91938748 0.92254613
 0.92562683 0.92860733 0.93155281 0.93440013 0.93719842 0.93992162
 0.94260398 0.94523008 0.94780201 0.95027963 0.95268493 0.95506498
 0.95741871 0.95971016 0.96183168 0.96385215 0.96583001 0.96771938
 0.96952409 0.97123504 0.97286791 0.97440417 0.97592036 0.97739382
 0.97884935 0.98027656 0.98159147 0.98287434 0.98413074 0.98531594
 0.98648663 0.98759508 0.98861041 0.98958371 0.99052784 0.99130616
 0.99205634 0.99277701 0.9934626  0.99412347 0.99474253 0.99526762
 0.995756   0.9962308  0.99666868 0.9970901  0.99743106 0.99776086
 0.99807616 0.99836135 0.99861277 0.99884791 0.99904145 0.99921677
 0.99937525 0.99952298 0.99965199 0.99977511 0.99985836 0.99991644
 0.99995328 0.99998046 0.99999116 0.99999488 0.99999787 1.
 1.        ]


'PLOT - CUMULATIVE VARIATON EXPLAINED vs EIGEN VALUE'
cum_val=np.cumsum(pca.explained_variance_ratio_)
plt.figure(figsize=(15,11));
plt.step(range(1,158),cum_val);
plt.ylabel('Cum of variation explained');
plt.xlabel('eigen Value');
plt.show();


pca=PCA(n_components=120)
pca.fit(x_train)

PCA(n_components=120)

PCA(n_components=120)


x_trainp=pca.transform(x_train)
x_testp=pca.transform(x_test)


param_grid = {'max_depth':[1,2,3,4,5,6,7,8,9,10,11,12],
              'min_samples_leaf':[1,2,3,4,5],
              'min_samples_split':[1,2,3,4,5,6]}
grid = GridSearchCV(dTree, param_grid,cv=10)
%time grid.fit(x_trainp, y_train)
print(grid.best_params_)
dTreebest = grid.best_estimator_
y_predict_Grid = dTreebest.predict(x_testp)

CPU times: total: 6min
Wall time: 20min 27s
{'max_depth': 12, 'min_samples_leaf': 1, 'min_samples_split': 1}


print(dTreebest.score(x_trainp,y_train))
print(dTreebest.score(x_testp,y_test))

0.9794895168641751
0.8743169398907104


from sklearn.metrics import  classification_report

print(classification_report(y_test, y_predict_Grid, digits=2))

              precision    recall  f1-score   support

          -1       0.92      0.82      0.87       366
           1       0.84      0.93      0.88       366

    accuracy                           0.87       732
   macro avg       0.88      0.87      0.87       732
weighted avg       0.88      0.87      0.87       732


from sklearn.metrics import accuracy_score,recall_score,precision_score,f1_score,classification_report
Accu_score=accuracy_score(y_test,y_predict_Grid)*100
recall=(recall_score(y_test,y_predict_Grid)*100)
precision=(precision_score(y_test,y_predict_Grid)*100)
f1score=f1_score(y_test,y_predict_Grid)*100


print("Accuracy of Decision Tree is %0.3f"%Accu_score)
print("Misclassification Rate of Decision Tree Model is %0.3f"%(100- Accu_score))
print("F1-Score of Decision Tree is %0.3f"%f1score)
print("Recall( =TP/(TP+FN)) is %0.3f "% recall)
print("Precision( =TP/(TP+FP)) is %0.3f" %precision)

Accuracy of Decision Tree is 87.432
Misclassification Rate of Decision Tree Model is 12.568
F1-Score of Decision Tree is 88.052
Recall( =TP/(TP+FN)) is 92.623 
Precision( =TP/(TP+FP)) is 83.911


from sklearn.metrics import confusion_matrix
conf_mat = confusion_matrix(y_test, y_predict_Grid)
df_conf_mat = pd.DataFrame(conf_mat,
                           index=['Actual -1', 'Actual 1'],
                           columns=['predicted -1', 'predicted 1',])
plt.figure(figsize = (7,5))
sns.heatmap(df_conf_mat,annot=True,fmt='.9g');


resultsDf=pd.DataFrame()
tempResultsDf = pd.DataFrame({'Method':['Decision Tree'],'Train Accuracy': dTreebest.score(x_trainp,y_train),'Test Accuracy': Accu_score,'Recall':recall,'Precision':precision,'F1-Score':f1score})
resultsDf = pd.concat([resultsDf, tempResultsDf],  ignore_index = True)
resultsDf


from sklearn.linear_model import LogisticRegression
lr=LogisticRegression()


lr.fit(x_trainp,y_train)
y_pred=lr.predict(x_testp)


print("Logistical regresiion Train score:",lr.score(x_trainp,y_train))
print("Logistical regresiion Test score:",lr.score(x_testp,y_test))

Logistical regresiion Train score: 0.8751139471285324
Logistical regresiion Test score: 0.8237704918032787


from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
kfold = KFold(n_splits=10, random_state=77,shuffle=True)
results = cross_val_score(lr,X, y, cv=kfold,)
print(results)
print(np.mean(abs(results)))
print(results.std())

[0.95541401 0.92993631 0.9044586  0.92356688 0.94904459 0.95541401
 0.91719745 0.90384615 0.90384615 0.96153846]
0.9304262616364527
0.022074878838925303


Accu_score2=accuracy_score(y_test,y_pred)*100
recall2=(recall_score(y_test,y_pred)*100)
precision2=(precision_score(y_test,y_pred)*100)
f1score2=(f1_score(y_test,y_pred)*100)


print("Accuracy of Logistic Regression Model is %0.3f"%Accu_score2)
print("Misclassification Rate of Logistic Regression Model is %0.3f"%(100- Accu_score2))
print("F!-score of Logistic Regression Model is %0.3f"%f1score2)
print("Recall( =TP/(TP+FN)) is %0.3f "% recall2)
print("Precision( =TP/(TP+FP)) is %0.3f" %precision2)

Accuracy of Logistic Regression Model is 82.377
Misclassification Rate of Logistic Regression Model is 17.623
F!-score of Logistic Regression Model is 83.004
Recall( =TP/(TP+FN)) is 86.066 
Precision( =TP/(TP+FP)) is 80.153


print('CLASSIFICATION REPORT FOR LOGISTIC REGRESSION')
print(classification_report(y_test, y_pred, digits=2))

CLASSIFICATION REPORT FOR LOGISTIC REGRESSION
              precision    recall  f1-score   support

          -1       0.85      0.79      0.82       366
           1       0.80      0.86      0.83       366

    accuracy                           0.82       732
   macro avg       0.83      0.82      0.82       732
weighted avg       0.83      0.82      0.82       732


cm=confusion_matrix(y_test, y_pred)

dcm=pd.DataFrame(cm,index=['Actual_0', 'Actual_1'],columns=['Predicted_0', 'Predicted_1',])
plt.figure(figsize = (7,5))
sns.heatmap(dcm,annot=True,fmt='.9g');


tempResultsDf = pd.DataFrame({'Method':['Logistic Regression'],'Train Accuracy': lr.score(x_trainp,y_train),'Test Accuracy': Accu_score2,'Recall':recall2,'Precision':precision2,'F1-Score':f1score2})
resultsDf = pd.concat([resultsDf, tempResultsDf],  ignore_index = True)
resultsDf


from sklearn.neighbors import KNeighborsClassifier
knn=KNeighborsClassifier()


param_grid = {'n_neighbors': [3,5,7,9,11,13,19,21,23,25,27,29],
             'metric':['euclidean','manhattan']}

grid = GridSearchCV(knn, param_grid,cv=10) 
%time grid.fit(x_trainp, y_train)
print(grid.best_params_)
Kbest = grid.best_estimator_
y_predict_Grid = Kbest.predict(x_testp)

CPU times: total: 1min 5s
Wall time: 11.7 s
{'metric': 'euclidean', 'n_neighbors': 3}


print("KNN Train score:",Kbest.score(x_trainp,y_train))
print("KNN Test score:",Kbest.score(x_testp,y_test))

KNN Train score: 0.7752962625341842
KNN Test score: 0.6680327868852459


Accu_score3=accuracy_score(y_test,y_predict_Grid)*100
recall3=(recall_score(y_test,y_predict_Grid)*100)
precision3=(precision_score(y_test,y_predict_Grid)*100)
f1score3=f1_score(y_test,y_predict_Grid)*100

print("Accuracy of KNN is %0.3f"%Accu_score3)
print("Misclassification Rate of KNN Model is %0.3f"%(100- Accu_score3))
print("F1-Score of KNN is %0.3f"%f1score3)
print("Recall( =TP/(TP+FN)) is %0.3f "% recall3)
print("Precision( =TP/(TP+FP)) is %0.3f" %precision3)

Accuracy of KNN is 66.803
Misclassification Rate of KNN Model is 33.197
F1-Score of KNN is 75.077
Recall( =TP/(TP+FN)) is 100.000 
Precision( =TP/(TP+FP)) is 60.099


print('CLASSIFICATION REPORT FOR KNN')
print(classification_report(y_test, y_predict_Grid, digits=2))

CLASSIFICATION REPORT FOR KNN
              precision    recall  f1-score   support

          -1       1.00      0.34      0.50       366
           1       0.60      1.00      0.75       366

    accuracy                           0.67       732
   macro avg       0.80      0.67      0.63       732
weighted avg       0.80      0.67      0.63       732


cm=confusion_matrix(y_test, y_predict_Grid)
dcm=pd.DataFrame(cm,index=['Actual_0', 'Actual_1'],columns=['Predicted_0', 'Predicted_1',])
plt.figure(figsize = (7,5))
sns.heatmap(dcm,annot=True,fmt='.9g');


tempResultsDf = pd.DataFrame({'Method':['KNN'], 'Train Accuracy': '0.93','Test Accuracy': Accu_score3,'Recall':recall3,'Precision':precision3,'F1-Score':f1score3})
resultsDf = pd.concat([resultsDf, tempResultsDf],  ignore_index = True)
resultsDf


from sklearn.svm import SVC
svm=SVC()


param_grid={ 'C':[0.1,1,10,100],
            'gamma':[0.01,0.1,1,10],
            'kernel':['rbf','poly']
}
grid=GridSearchCV(svm,param_grid,cv=10)  
grid.fit(x_trainp,y_train)

GridSearchCV(cv=10, estimator=SVC(),
             param_grid={'C': [0.1, 1, 10, 100], 'gamma': [0.01, 0.1, 1, 10],
                         'kernel': ['rbf', 'poly']})

GridSearchCV(cv=10, estimator=SVC(),
             param_grid={'C': [0.1, 1, 10, 100], 'gamma': [0.01, 0.1, 1, 10],
                         'kernel': ['rbf', 'poly']})

SVC()

SVC()


print(grid.best_params_)
SVMbest=grid.best_estimator_
SVMbest
y_predict_grid=SVMbest.predict(x_testp)

{'C': 10, 'gamma': 0.01, 'kernel': 'rbf'}


print("SVM Train score:",SVMbest.score(x_trainp, y_train))
print("SVM Test score:",SVMbest.score(x_testp, y_test))

SVM Train score: 1.0
SVM Test score: 0.9959016393442623


print(classification_report(y_test, y_predict_grid, digits=2))

              precision    recall  f1-score   support

          -1       1.00      0.99      1.00       366
           1       0.99      1.00      1.00       366

    accuracy                           1.00       732
   macro avg       1.00      1.00      1.00       732
weighted avg       1.00      1.00      1.00       732


Accu_score4=accuracy_score(y_test,y_predict_grid)*100
recall4=(recall_score(y_test,y_predict_grid)*100)
precision4=(precision_score(y_test,y_predict_grid)*100)
f1score4=f1_score(y_test,y_predict_grid)*100

print("Accuracy of SVM is %0.3f"%Accu_score4)
print("Misclassification Rate of SVM Model is %0.3f"%(100- Accu_score4))
print("F1-Score of SVM is %0.3f"%f1score4)
print("Recall( =TP/(TP+FN)) is %0.3f "% recall4)
print("Precision( =TP/(TP+FP)) is %0.3f" %precision4)

Accuracy of SVM is 99.590
Misclassification Rate of SVM Model is 0.410
F1-Score of SVM is 99.592
Recall( =TP/(TP+FN)) is 100.000 
Precision( =TP/(TP+FP)) is 99.187


conf_mat = confusion_matrix(y_test, y_predict_grid)
df_conf_mat = pd.DataFrame(conf_mat,
                           index=['actual_0', 'actual_1'],
                           columns=['predicted_0', 'predicted_1',])
plt.figure(figsize = (7,5))
sns.heatmap(df_conf_mat,annot=True,fmt='.9g');


tempResultsDf = pd.DataFrame({'Method':['SVM'], 'Train Accuracy': '1.0','Test Accuracy': Accu_score4,'Recall':recall4,'Precision':precision4,'F1-Score':f1score4})
resultsDf = pd.concat([resultsDf, tempResultsDf],  ignore_index = True)
resultsDf


from sklearn.ensemble import RandomForestClassifier


rfc = RandomForestClassifier(n_estimators = 50, random_state=12)
rfc = rfc.fit(x_trainp, y_train)

y_predict = rfc.predict(x_testp)


print("Random forest Train score:",rfc.score(x_trainp , y_train))
print("Random forest Test score:",rfc.score(x_testp , y_test))

Random forest Train score: 1.0
Random forest Test score: 0.9795081967213115


from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score

kfold = KFold(n_splits=10, random_state=77,shuffle=True)
results = cross_val_score(rfc,X, y, cv=kfold)


print(results)

print(np.mean(abs(results)))
print(results.std())

[0.96178344 0.92993631 0.91719745 0.92356688 0.95541401 0.94904459
 0.91719745 0.90384615 0.91025641 0.96153846]
0.9329781153029562
0.020899212281438314


from sklearn.metrics import accuracy_score,recall_score,precision_score,f1_score,confusion_matrix
Accu_score5=accuracy_score(y_test,y_predict)*100
recall5=(recall_score(y_test,y_predict)*100)
precision5=(precision_score(y_test,y_predict)*100)
f1score5=f1_score(y_test,y_predict)*100

print("Accuracy of Random Forest Classifier  is %0.3f"%Accu_score5)
print("Misclassification Rate of Random Forest Classifier Model is %0.3f"%(100- Accu_score5))
print("F1-Score of SVM is %0.3f"%f1score5)
print("Recall( =TP/(TP+FN)) is %0.3f "% recall5)
print("Precision( =TP/(TP+FP)) is %0.3f" %precision5)

Accuracy of Random Forest Classifier  is 97.951
Misclassification Rate of Random Forest Classifier Model is 2.049
F1-Score of SVM is 97.914
Recall( =TP/(TP+FN)) is 96.175 
Precision( =TP/(TP+FP)) is 99.717


confusion_matrix(y_test, y_predict)

array([[365,   1],
       [ 14, 352]], dtype=int64)


cm=confusion_matrix(y_test, y_predict,labels=[-1, 1])

df_cm = pd.DataFrame(cm, index = [i for i in ["No","Yes"]],
                  columns = [i for i in ["No","Yes"]])
plt.figure(figsize = (7,5))
sns.heatmap(df_cm, annot=True ,fmt='g')

<Axes: >


tempResultsDf = pd.DataFrame({'Method':['Random Forest Classifier'], 'Train Accuracy': '1.0','Test Accuracy': Accu_score5,'Recall':recall5,'Precision':precision5,'F1-Score':f1score5})
resultsDf = pd.concat([resultsDf, tempResultsDf],  ignore_index = True)
resultsDf


from sklearn.ensemble import AdaBoostClassifier
abc = AdaBoostClassifier(n_estimators=40, random_state=1)

abc = abc.fit(x_trainp, y_train)


y_predict = abc.predict(x_testp)


print("Adaboost Train score:",abc.score(x_trainp , y_train))
print("Adaboost Test score:",abc.score(x_testp , y_test))

Adaboost Train score: 0.96718322698268
Adaboost Test score: 0.9275956284153005


from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score

kfold = KFold(n_splits=10, random_state=77,shuffle=True)
results = cross_val_score(abc,X, y, cv=kfold)


print(results)
print(np.mean(abs(results)))
print(results.std())

[0.92356688 0.91082803 0.87261146 0.91719745 0.94904459 0.93630573
 0.92356688 0.88461538 0.91666667 0.92948718]
0.9163890249877511
0.021668779540414712


Accu_score6=accuracy_score(y_test,y_predict)*100
recall6=(recall_score(y_test,y_predict)*100)
precision6=(precision_score(y_test,y_predict)*100)
f1score6=f1_score(y_test,y_predict)*100

print("Accuracy of Adaboost Classifier  is %0.3f"%Accu_score6)
print("Misclassification Rate of Adaboost Classifier Model is %0.3f"%(100- Accu_score6))
print("F1-Score of SVM is %0.3f"%f1score6)
print("Recall( =TP/(TP+FN)) is %0.3f "% recall6)
print("Precision( =TP/(TP+FP)) is %0.3f" %precision6)

Accuracy of Adaboost Classifier  is 92.760
Misclassification Rate of Adaboost Classifier Model is 7.240
F1-Score of SVM is 92.587
Recall( =TP/(TP+FN)) is 90.437 
Precision( =TP/(TP+FP)) is 94.842


cm=confusion_matrix(y_test, y_predict,labels=[-1, 1])

df_cm = pd.DataFrame(cm, index = [i for i in ["No","Yes"]],
                  columns = [i for i in ["No","Yes"]])
plt.figure(figsize = (7,5))
sns.heatmap(df_cm, annot=True ,fmt='g')

<Axes: >


tempResultsDf = pd.DataFrame({'Method':['Adaboost Classifier'], 'Train Accuracy': '0.89','Test Accuracy': Accu_score6,'Recall':recall6,'Precision':precision6,'F1-Score':f1score6})
resultsDf = pd.concat([resultsDf, tempResultsDf],  ignore_index = True)
resultsDf


resultsDf


from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer


pipeline = Pipeline([
                    ('scl', StandardScaler()), 
                    ('pca', PCA(n_components=120)),
                    ('clf', SVC(C=10, gamma= 0.1, kernel= 'rbf'))])


pipeline.fit(x_train,y_train)

Pipeline(steps=[('scl', StandardScaler()), ('pca', PCA(n_components=120)),
                ('clf', SVC(C=10, gamma=0.1))])

Pipeline(steps=[('scl', StandardScaler()), ('pca', PCA(n_components=120)),
                ('clf', SVC(C=10, gamma=0.1))])

StandardScaler()

PCA(n_components=120)

SVC(C=10, gamma=0.1)


import pickle
saved_model = pickle.dumps(pipeline)


# load the model from disk
loaded_model = pickle.loads(saved_model)

	Time	0	1	2	3	4	5	6	7	8	...	581	582	583	584	585	586	587	588	589	Pass/Fail
0	2008-07-19 11:55:00	3030.93	2564.00	2187.7333	1411.1265	1.3602	100.0	97.6133	0.1242	1.5005	...	NaN	0.5005	0.0118	0.0035	2.3630	NaN	NaN	NaN	NaN	-1
1	2008-07-19 12:32:00	3095.78	2465.14	2230.4222	1463.6606	0.8294	100.0	102.3433	0.1247	1.4966	...	208.2045	0.5019	0.0223	0.0055	4.4447	0.0096	0.0201	0.0060	208.2045	-1
2	2008-07-19 13:17:00	2932.61	2559.94	2186.4111	1698.0172	1.5102	100.0	95.4878	0.1241	1.4436	...	82.8602	0.4958	0.0157	0.0039	3.1745	0.0584	0.0484	0.0148	82.8602	1
3	2008-07-19 14:43:00	2988.72	2479.90	2199.0333	909.7926	1.3204	100.0	104.2367	0.1217	1.4882	...	73.8432	0.4990	0.0103	0.0025	2.0544	0.0202	0.0149	0.0044	73.8432	-1
4	2008-07-19 15:22:00	3032.24	2502.87	2233.3667	1326.5200	1.5334	100.0	100.3967	0.1235	1.5031	...	NaN	0.4800	0.4766	0.1045	99.3032	0.0202	0.0149	0.0044	73.8432	-1

	0	1	2	3	4	5	6	7	8	9	...	581	582	583	584	585	586	587	588	589	Pass/Fail
count	1561.000000	1560.000000	1553.000000	1553.000000	1553.000000	1553.0	1553.000000	1558.000000	1565.000000	1565.000000	...	618.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1567.000000
mean	3014.452896	2495.850231	2200.547318	1396.376627	4.197013	100.0	101.112908	0.121822	1.462862	-0.000841	...	97.934373	0.500096	0.015318	0.003847	3.067826	0.021458	0.016475	0.005283	99.670066	-0.867262
std	73.621787	80.407705	29.513152	441.691640	56.355540	0.0	6.237214	0.008961	0.073897	0.015116	...	87.520966	0.003404	0.017180	0.003720	3.578033	0.012358	0.008808	0.002867	93.891919	0.498010
min	2743.240000	2158.750000	2060.660000	0.000000	0.681500	100.0	82.131100	0.000000	1.191000	-0.053400	...	0.000000	0.477800	0.006000	0.001700	1.197500	-0.016900	0.003200	0.001000	0.000000	-1.000000
25%	2966.260000	2452.247500	2181.044400	1081.875800	1.017700	100.0	97.920000	0.121100	1.411200	-0.010800	...	46.184900	0.497900	0.011600	0.003100	2.306500	0.013425	0.010600	0.003300	44.368600	-1.000000
50%	3011.490000	2499.405000	2201.066700	1285.214400	1.316800	100.0	101.512200	0.122400	1.461600	-0.001300	...	72.288900	0.500200	0.013800	0.003600	2.757650	0.020500	0.014800	0.004600	71.900500	-1.000000
75%	3056.650000	2538.822500	2218.055500	1591.223500	1.525700	100.0	104.586700	0.123800	1.516900	0.008400	...	116.539150	0.502375	0.016500	0.004100	3.295175	0.027600	0.020300	0.006400	114.749700	-1.000000
max	3356.350000	2846.440000	2315.266700	3715.041700	1114.536600	100.0	129.252200	0.128600	1.656400	0.074900	...	737.304800	0.509800	0.476600	0.104500	99.303200	0.102800	0.079900	0.028600	737.304800	1.000000

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32	33	34	35	36	37	38	39	40	41	42	43	44	45	46	47	48	49	50	51	52	53	54	55	56	57	58	59	60	61	62	63	64	65	66	67	68	69	70	71	72	73	74	75	76	77	78	79	80	81	82	83	84	85	86	87	88	89	90	91	92	93	94	95	96	97	98	99	100	101	102	103	104	105	106	107	108	109	110	111	112	113	114	115	116	117	118	119	120	121	122	123	124	125	126	127	128	129	130	131	132	133	134	135	136	137	138	139	140	141	142	143	144	145	146	147	148	149	150	151	152	153	154	155	156	157	158	159	160	161	162	163	164	165	166	167	168	169	170	171	172	173	174	175	176	177	178	179	180	181	182	183	184	185	186	187	188	189	190	191	192	193	194	195	196	197	198	199	200	201	202	203	204	205	206	207	208	209	210	211	212	213	214	215	216	217	218	219	220	221	222	223	224	225	226	227	228	229	230	231	232	233	234	235	236	237	238	239	240	241	242	243	244	245	246	247	248	249	250	251	252	253	254	255	256	257	258	259	260	261	262	263	264	265	266	267	268	269	270	271	272	273	274	275	276	277	278	279	280	281	282	283	284	285	286	287	288	289	290	291	292	293	294	295	296	297	298	299	300	301	302	303	304	305	306	307	308	309	310	311	312	313	314	315	316	317	318	319	320	321	322	323	324	325	326	327	328	329	330	331	332	333	334	335	336	337	338	339	340	341	342	343	344	345	346	347	348	349	350	351	352	353	354	355	356	357	358	359	360	361	362	363	364	365	366	367	368	369	370	371	372	373	374	375	376	377	378	379	380	381	382	383	384	385	386	387	388	389	390	391	392	393	394	395	396	397	398	399	400	401	402	403	404	405	406	407	408	409	410	411	412	413	414	415	416	417	418	419	420	421	422	423	424	425	426	427	428	429	430	431	432	433	434	435	436	437	438	439	440	441	442	443	444	445	446	447	448	449	450	451	452	453	454	455	456	457	458	459	460	461	462	463	464	465	466	467	468	469	470	471	472	473	474	475	476	477	478	479	480	481	482	483	484	485	486	487	488	489	490	491	492	493	494	495	496	497	498	499	500	501	502	503	504	505	506	507	508	509	510	511	512	513	514	515	516	517	518	519	520	521	522	523	524	525	526	527	528	529	530	531	532	533	534	535	536	537	538	539	540	541	542	543	544	545	546	547	548	549	550	551	552	553	554	555	556	557	558	559	560	561	562	563	564	565	566	567	568	569	570	571	572	573	574	575	576	577	578	579	580	581	582	583	584	585	586	587	588	589	Pass/Fail
count	1561.000000	1560.000000	1553.000000	1553.000000	1553.000000	1553.000000	1553.000000	1558.000000	1565.000000	1565.000000	1565.000000	1565.000000	1565.000000	1564.000000	1564.000000	1564.000000	1564.000000	1564.000000	1564.000000	1557.000000	1567.000000	1565.000000	1565.000000	1565.000000	1565.000000	1565.000000	1565.000000	1565.000000	1565.000000	1565.000000	1565.000000	1565.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1543.000000	1543.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1563.000000	1563.000000	1563.000000	1563.000000	1563.000000	1563.000000	1560.000000	1561.000000	1561.000000	1561.000000	1560.000000	1560.000000	1560.000000	1561.000000	1561.000000	1561.000000	1561.000000	1561.000000	1561.000000	773.000000	773.000000	1561.000000	1543.000000	1543.000000	1543.000000	1543.000000	1543.000000	1543.000000	1543.000000	1543.000000	1566.000000	1555.000000	226.000000	1567.000000	1567.000000	1567.000000	1516.000000	1516.000000	1561.000000	1565.000000	1565.000000	1561.000000	1561.000000	1561.000000	1561.000000	1561.000000	1561.000000	1561.000000	1561.000000	1561.000000	1565.000000	1565.000000	1561.000000	1561.000000	1561.000000	1561.000000	549.000000	549.000000	549.000000	852.000000	1567.000000	1567.000000	1567.000000	1567.000000	1567.000000	1543.000000	1567.000000	1567.000000	1558.000000	1558.000000	1558.000000	1558.000000	1558.000000	1558.000000	1558.000000	1558.000000	1558.000000	1558.000000	1558.000000	1559.000000	1559.000000	1559.000000	1562.000000	1561.000000	1560.000000	1553.000000	1553.000000	1553.000000	1553.000000	1553.000000	1558.000000	1565.000000	1565.000000	1565.000000	1565.000000	1565.000000	1564.000000	1564.000000	1564.000000	1564.000000	1564.000000	1564.000000	1557.000000	1567.000000	138.000000	138.000000	1565.000000	1565.000000	1565.000000	1565.000000	1565.000000	1565.000000	1565.000000	1565.000000	1565.000000	1565.000000	1565.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1543.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1563.000000	1563.000000	1563.000000	1563.000000	1563.000000	1563.000000	1560.000000	1561.000000	1561.000000	1561.000000	1560.000000	1560.000000	1560.000000	1561.000000	1561.000000	1561.000000	1561.000000	1561.000000	1561.000000	1561.000000	1543.000000	1543.000000	1543.000000	1543.000000	1543.000000	1543.000000	1543.000000	1543.000000	1566.000000	1555.000000	226.000000	1567.000000	1567.000000	1567.000000	1516.000000	1516.000000	1561.000000	1565.000000	1565.000000	1561.000000	1561.000000	1561.000000	1561.000000	1561.000000	1561.000000	1561.000000	1561.000000	1561.000000	1565.000000	1565.000000	1561.000000	1561.000000	1561.000000	1561.000000	549.000000	549.000000	549.000000	852.000000	1567.000000	1567.000000	1567.000000	1567.000000	1567.000000	1543.000000	1567.000000	1567.000000	1558.000000	1558.000000	1558.000000	1558.000000	1558.000000	1558.000000	1558.000000	1558.000000	1558.000000	1558.000000	1558.000000	1559.000000	1559.000000	1559.000000	1562.000000	1561.000000	1560.000000	1553.000000	1553.000000	1553.000000	1553.000000	1553.000000	1558.000000	1565.000000	1565.000000	1565.000000	1565.000000	1565.000000	1564.000000	1564.000000	1564.000000	1564.000000	1564.000000	1564.000000	1557.000000	1567.000000	138.000000	138.000000	1565.000000	1565.000000	1565.000000	1565.000000	1565.000000	1565.000000	1565.000000	1565.000000	1565.000000	1565.000000	1565.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1543.000000	1543.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1563.000000	1563.000000	1563.000000	1563.000000	1563.000000	1563.000000	1560.000000	1561.000000	1561.000000	1561.000000	1560.000000	1560.000000	1560.000000	1561.000000	1561.000000	1561.000000	1561.000000	1561.000000	1561.000000	773.000000	773.000000	1561.000000	1543.000000	1543.000000	1543.000000	1543.000000	1543.000000	1543.000000	1543.000000	1543.000000	1566.000000	1555.000000	226.000000	1567.000000	1567.000000	1567.000000	1516.000000	1516.000000	1561.000000	1565.000000	1565.000000	1561.000000	1561.000000	1561.000000	1561.000000	1561.000000	1561.000000	1561.000000	1561.000000	1561.000000	1565.000000	1565.000000	1561.000000	1561.000000	1561.000000	1561.000000	549.000000	549.000000	549.000000	852.000000	1567.000000	1567.000000	1567.000000	1567.000000	1567.000000	1543.000000	1567.000000	1567.000000	1558.000000	1558.000000	1558.000000	1558.000000	1558.000000	1558.000000	1558.000000	1558.000000	1558.000000	1558.000000	1558.000000	1559.000000	1559.000000	1559.000000	1562.000000	1561.000000	1560.000000	1553.000000	1553.000000	1553.000000	1553.000000	1553.000000	1558.000000	1565.000000	1565.000000	1565.000000	1565.000000	1565.000000	1564.000000	1564.000000	1564.000000	1564.000000	1564.000000	1564.000000	1557.000000	1567.000000	1565.000000	1565.000000	1565.000000	1565.000000	1565.000000	1565.000000	1565.000000	1565.000000	1565.000000	1565.000000	1565.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1543.000000	1543.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1563.000000	1563.000000	1563.000000	1563.000000	1563.000000	1563.000000	1560.000000	1561.000000	1561.000000	1561.000000	1560.000000	1560.000000	1560.000000	1561.000000	1561.000000	1561.000000	1561.000000	1561.000000	1561.000000	1561.000000	1543.000000	1543.000000	1543.000000	1543.000000	1543.000000	1543.000000	1543.000000	1543.000000	1566.000000	1555.000000	226.000000	1567.000000	1567.000000	1567.000000	1516.000000	1516.000000	1561.000000	1565.000000	1565.000000	1561.000000	1561.000000	1561.000000	1561.000000	1561.000000	1561.000000	1561.000000	1561.000000	1561.000000	1565.000000	1565.000000	1561.000000	1561.000000	1561.000000	1561.000000	549.000000	549.000000	549.000000	852.000000	1567.000000	1567.000000	1567.000000	1567.000000	1567.000000	1543.000000	1567.000000	1567.000000	1558.000000	1558.000000	1558.000000	1558.000000	1558.000000	1558.000000	1558.000000	1558.000000	1558.000000	1558.000000	1558.000000	1559.000000	1559.000000	1559.000000	1565.000000	1565.000000	1565.000000	1565.000000	1307.000000	1307.000000	1307.000000	1307.000000	1307.000000	1307.000000	1307.000000	1307.000000	1307.000000	1307.000000	1307.000000	1307.000000	1566.000000	1566.000000	1566.000000	1566.000000	1294.000000	1294.000000	1294.000000	1294.000000	1294.000000	1294.000000	1294.000000	1294.000000	1567.000000	1567.000000	1567.000000	1567.000000	1567.000000	1567.000000	1567.000000	1567.000000	618.000000	618.000000	618.000000	618.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1566.000000	1567.000000
mean	3014.452896	2495.850231	2200.547318	1396.376627	4.197013	100.000000	101.112908	0.121822	1.462862	-0.000841	0.000146	0.964353	199.956809	0.000000	9.005371	413.086035	9.907603	0.971444	190.047354	12.481034	1.405054	-5618.393610	2699.378435	-3806.299734	-298.598136	1.203845	1.938477	6.638628	69.499532	2.366197	0.184159	3.673189	85.337469	8.960279	50.582639	64.555787	49.417370	66.221274	86.836577	118.679554	67.904909	3.353066	70.000000	355.538904	10.031165	136.743060	733.672811	1.177958	139.972231	1.000000	632.254197	157.420991	0.000000	4.592971	4.838523	2856.172105	0.928849	0.949215	4.593312	2.960241	355.159094	10.423143	116.502329	13.989927	20.542109	27.131816	706.668523	16.715444	147.437578	1.000000	619.101687	104.329033	150.361552	468.020404	0.002688	-0.006903	-0.029390	-0.007041	-0.013643	0.003458	-0.018531	-0.021153	0.006055	7.452067	0.133108	0.112783	2.401872	0.982420	1807.815021	0.188703	8827.536865	0.002440	0.000507	-0.000541	-0.000029	0.000060	0.017127	0.000000	-0.018143	0.001540	-0.000021	-0.000007	0.001115	-0.009789	-0.000015	-0.000498	0.000540	-0.001766	-0.010789	0.979993	101.318253	231.818898	0.457538	0.945424	0.000123	747.383792	0.987130	58.625908	0.598412	0.970777	6.310863	15.796425	3.898390	15.829660	15.794705	1.184956	2.750728	0.648478	3.192182	-0.554228	0.744976	0.997808	2.318545	1004.043093	39.391979	117.960948	138.194747	122.692949	57.603025	416.766964	26.077904	0.000000	6.641565	0.004169	0.120008	0.063621	0.055010	0.017411	8.471308	0.000000	6.814268	14.047403	1.196733	0.011926	7.697971	0.507171	0.058089	0.047104	1039.650738	882.680511	555.346326	4066.850479	4797.154633	0.140204	0.127942	0.252026	2.788882	1.235783	0.124397	0.400454	0.684330	0.120064	0.320113	0.576192	0.320113	0.778044	0.244718	0.394760	0.000000	0.000000	19.013257	0.546770	10.780543	26.661170	0.144815	7.365741	0.000000	17.936290	43.211418	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.287084	8.688487	20.092710	0.557359	11.532056	17.600192	7.839359	10.170463	30.073143	32.218169	9.050122	0.001281	20.376176	73.264316	0.029564	0.088866	0.056755	0.051432	0.060346	0.083268	0.081076	0.083484	0.071635	3.771465	0.003254	0.009213	0.060718	0.008821	122.846571	0.059370	1041.056588	0.000000	0.019125	0.017844	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.004791	0.004575	0.000000	0.000000	0.000000	0.000000	0.005755	1.729723	4.148742	0.053374	0.025171	0.001065	109.650967	0.004285	4.645115	0.033216	0.013943	0.403848	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.070587	19.504677	3.777866	29.260291	46.056598	41.298147	20.181246	136.292426	8.693213	0.000000	2.210744	0.001117	0.041057	0.018034	0.015094	0.005770	2.803984	0.000000	2.119795	4.260018	0.367529	0.003924	2.578596	0.123427	0.019926	0.014487	335.551157	401.814750	252.999118	1879.228369	2342.826978	0.063804	0.060267	0.118386	0.910146	0.403342	0.040344	0.132076	0.264917	0.048623	0.128921	0.218414	0.128921	0.304752	0.097344	0.160051	0.000000	0.000000	0.000000	5.976977	0.172629	3.188770	7.916036	0.043105	2.263727	0.000000	5.393420	13.332172	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.083232	2.593485	6.215866	0.168364	3.426925	9.736386	2.327482	3.037580	9.328958	14.673507	2.732094	0.000286	6.198508	23.217146	7.958376	5.770212	0.008914	0.024706	0.025252	0.023202	0.027584	0.023356	0.040331	0.041921	0.034543	1.298629	0.000999	0.002443	0.019840	0.002945	39.936406	0.018383	333.319601	0.000000	0.005199	0.004814	0.003773	0.003172	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.001601	0.001571	0.000000	0.000000	0.000000	0.000000	0.001826	0.541040	1.285448	0.011427	0.008281	0.000339	35.155091	0.001338	1.431868	0.010956	0.004533	0.133990	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.024208	6.730615	1.231997	5.340932	4.580430	4.929344	2.616086	30.911316	25.612690	0.000000	6.630616	3.404349	8.190905	320.259235	309.061299	1.821261	4.174524	0.000000	77.660446	3.315469	6.796312	1.233858	4.058501	4.220747	4.171844	18.421600	22.358305	99.367633	205.519304	14.733945	9.370666	7.513266	4.016785	54.701052	70.643942	11.526617	0.802081	1.345259	0.633941	0.895043	0.647090	1.175003	0.281895	0.332270	0.000000	0.000000	0.000000	5.346816	5.460971	7.883742	3.636633	12.325685	5.263666	0.000000	2.838380	29.197414	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	6.252091	224.173047	5.662293	5.367752	9.638797	137.888406	39.426847	37.637050	4.262573	20.132155	6.257921	0.128123	3.283394	75.538131	0.000000	318.418448	206.564196	215.288948	201.111728	302.506186	239.455326	352.616477	272.169707	51.354045	2.442673	8.170943	2.530046	0.956442	6.807826	29.865896	11.821030	0.000000	263.195864	240.981377	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	55.763508	275.979457	0.000000	0.000000	0.000000	0.000000	0.678898	1.738902	1.806273	11.728440	2.695999	11.610080	14.728866	0.453896	5.687782	5.560397	1.443457	6.395717	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.034235	1.942828	9.611628	0.111208	0.008471	0.002509	7.611403	1.039630	403.546477	75.679871	0.663256	17.013313	1.230712	0.276688	7.703874	0.503657	57.746537	4.216905	1.623070	0.995009	0.325708	0.072443	32.284956	262.729683	0.679641	6.444985	0.145610	2.610870	0.060086	2.452417	21.117674	530.523623	2.101836	28.450165	0.345636	9.162315	0.104729	5.563747	16.642363	0.021615	0.016829	0.005396	97.934373	0.500096	0.015318	0.003847	3.067826	0.021458	0.016475	0.005283	99.670066	-0.867262
std	73.621787	80.407705	29.513152	441.691640	56.355540	0.000000	6.237214	0.008961	0.073897	0.015116	0.009302	0.012452	3.257276	0.000000	2.796596	17.221095	2.403867	0.012062	2.781041	0.217965	0.016737	626.822178	295.498535	1380.162148	2902.690117	0.177600	0.189495	1.244249	3.461181	0.408694	0.032944	0.535322	2.026549	1.344456	1.182618	2.574749	1.182619	0.304141	0.446756	1.807221	24.062943	2.360425	0.000000	6.234706	0.175038	7.849247	12.170315	0.189637	4.524251	0.000000	8.643985	60.925108	0.000000	0.054950	0.059581	25.749317	0.006807	0.004176	0.085095	9.532220	6.027889	0.274877	8.629022	7.119863	4.977467	7.121703	11.623078	307.502293	4.240095	0.000000	9.539190	31.651899	18.388481	17.629886	0.106190	0.022292	0.033203	0.031368	0.047872	0.023080	0.049226	0.017021	0.036074	0.516251	0.005051	0.002928	0.037332	0.012848	53.537262	0.052373	396.313662	0.087683	0.003231	0.003010	0.000174	0.000104	0.219578	0.000000	0.427110	0.062740	0.000356	0.000221	0.062968	0.003065	0.000851	0.003202	0.002988	0.087475	0.086758	0.008695	1.880087	2.105318	0.048939	0.012133	0.001668	48.949250	0.009497	6.485174	0.008102	0.008949	0.124304	0.099618	0.904120	0.108315	0.114144	0.280555	0.253471	0.135409	0.264175	1.220479	0.082531	0.002251	0.053181	6.537701	2.990476	57.544627	53.909792	52.253015	12.345358	263.300614	506.922106	0.000000	3.552254	0.001282	0.061343	0.026541	0.021844	0.027123	18.740631	0.000000	3.241843	31.002541	23.364063	0.009346	5.239219	1.122427	0.079174	0.039538	406.848810	983.043021	574.808588	4239.245058	6553.569317	0.121989	0.242534	0.407329	1.119756	0.632767	0.047639	0.197918	0.157468	0.060785	0.071243	0.095734	0.071247	0.116322	0.074918	0.282903	0.000000	0.000000	3.311632	0.224402	4.164051	6.836101	0.110198	7.188720	0.000000	8.609912	21.711876	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.395187	15.720926	10.552162	0.537705	16.445556	8.690718	5.104495	14.622904	17.461798	565.101239	11.541083	0.050621	17.497556	28.067143	1.168074	0.042065	0.025005	0.031578	0.053030	0.056456	0.030437	0.025764	0.046283	1.170436	0.001646	0.001989	0.023305	0.055937	55.156003	0.071211	433.170076	0.000000	0.010756	0.010745	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.001698	0.001441	0.000000	0.000000	0.000000	0.000000	0.084618	4.335614	10.045084	0.066880	0.049235	0.015771	54.597274	0.037472	64.354756	0.022425	0.009132	0.120334	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.029649	7.344404	1.152329	8.402013	17.866438	17.737513	3.830463	85.607784	168.949413	0.000000	1.196437	0.000340	0.020289	0.006483	0.005545	0.008550	5.864324	0.000000	0.962923	9.763829	7.386343	0.002936	1.616993	0.270987	0.025549	0.011494	137.692483	477.050076	283.530702	1975.111365	3226.924298	0.064225	0.130825	0.219147	0.331982	0.197514	0.014511	0.064867	0.057387	0.025400	0.027468	0.033593	0.027470	0.043460	0.028796	0.117316	0.000000	0.000000	0.000000	1.018629	0.072392	1.215930	2.179059	0.031885	2.116994	0.000000	2.518859	6.615850	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.063425	5.645226	3.403447	0.172883	5.781558	7.556131	1.699435	5.645022	6.074702	261.738451	3.667902	0.011319	5.371825	8.895221	17.512965	17.077498	0.352186	0.011862	0.010603	0.014326	0.024563	0.013157	0.015499	0.013068	0.022307	0.386918	0.000501	0.000395	0.007136	0.020003	17.056304	0.021644	138.801928	0.000000	0.002656	0.002382	0.002699	0.002107	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000534	0.000467	0.000000	0.000000	0.000000	0.000000	0.026740	1.341020	3.168427	0.014366	0.015488	0.004989	17.227003	0.011816	20.326415	0.006738	0.002956	0.038408	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.010728	2.829583	0.364711	2.578118	1.776843	2.122978	0.551474	18.413622	47.308463	0.000000	3.958371	1.035433	4.054515	287.704482	325.448391	3.057692	6.913855	0.000000	32.596933	6.325365	23.257716	0.995620	3.042144	10.632730	6.435390	36.060084	36.395408	126.188715	225.778870	34.108854	34.369789	34.557804	1.611274	34.108051	38.376178	6.169471	0.184213	0.659195	0.143552	0.155522	0.141252	0.176158	0.086461	0.236275	0.000000	0.000000	0.000000	0.919196	2.250804	3.059660	0.938372	8.125876	4.537737	0.000000	1.345576	13.335189	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	8.673724	230.766915	3.151685	4.983367	10.174117	47.698041	22.457104	24.822918	2.611174	14.939590	10.185026	5.062075	2.638608	35.752493	0.000000	281.011323	192.864413	213.126638	218.690015	287.364070	263.837645	252.043751	228.046702	18.048612	1.224283	1.759262	0.973948	6.615200	3.260019	24.621586	4.956647	0.000000	324.771342	323.003410	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	37.691736	329.664680	0.000000	0.000000	0.000000	0.000000	10.783880	4.890663	4.715894	15.814420	5.702366	103.122996	7.104435	4.147581	20.663414	3.920370	0.958428	1.888698	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.252913	0.731928	2.896376	0.002737	0.001534	0.000296	1.315544	0.389066	5.063887	3.390523	0.673346	4.966954	1.361117	0.276231	2.192647	0.598852	35.207552	1.280008	1.870433	0.083860	0.201392	0.051578	19.026081	7.630585	0.121758	2.633583	0.081122	1.032761	0.032761	0.996644	10.213294	17.499736	0.275112	86.304681	0.248478	26.920150	0.067791	16.921369	12.485267	0.011730	0.009640	0.003116	87.520966	0.003404	0.017180	0.003720	3.578033	0.012358	0.008808	0.002867	93.891919	0.498010
min	2743.240000	2158.750000	2060.660000	0.000000	0.681500	100.000000	82.131100	0.000000	1.191000	-0.053400	-0.034900	0.655400	182.094000	0.000000	2.249300	333.448600	4.469600	0.579400	169.177400	9.877300	1.179700	-7150.250000	0.000000	-9986.750000	-14804.500000	0.000000	0.000000	0.000000	59.400000	0.666700	0.034100	2.069800	83.182900	7.603200	49.834800	63.677400	40.228900	64.919300	84.732700	111.712800	1.434000	-0.075900	70.000000	342.754500	9.464000	108.846400	699.813900	0.496700	125.798200	1.000000	607.392700	40.261400	0.000000	3.706000	3.932000	2801.000000	0.875500	0.931900	4.219900	-28.988200	324.714500	9.461100	81.490000	1.659100	6.448200	4.308000	632.422600	0.413700	87.025500	1.000000	581.777300	21.433200	-59.477700	456.044700	0.000000	-0.104900	-0.186200	-0.104600	-0.348200	-0.056800	-0.143700	-0.098200	-0.212900	5.825700	0.117400	0.105300	2.242500	0.774900	1627.471400	0.111300	7397.310000	-0.357000	-0.012600	-0.017100	-0.002000	-0.000900	-1.480300	0.000000	-5.271700	-0.528300	-0.003000	-0.002400	-0.535300	-0.032900	-0.011900	-0.028100	-0.013300	-0.522600	-0.345400	0.784800	88.193800	213.008300	0.000000	0.853400	0.000000	544.025400	0.890000	52.806800	0.527400	0.841100	5.125900	15.460000	1.671000	15.170000	15.430000	0.312200	2.340000	0.316100	0.000000	-3.779000	0.419900	0.993600	2.191100	980.451000	33.365800	58.000000	36.100000	19.200000	19.800000	0.000000	0.031900	0.000000	1.740000	0.000000	0.032400	0.021400	0.022700	0.004300	1.420800	0.000000	1.337000	2.020000	0.154400	0.003600	1.243800	0.140000	0.011100	0.011800	234.099600	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.800000	0.300000	0.033000	0.046000	0.297900	0.008900	0.128700	0.253800	0.128700	0.461600	0.073500	0.047000	0.000000	0.000000	9.400000	0.093000	3.170000	5.014000	0.029700	1.940000	0.000000	6.220000	6.613000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.080000	1.750000	9.220000	0.090000	2.770000	3.210000	0.000000	0.000000	7.728000	0.042900	2.300000	0.000000	4.010000	5.359000	0.000000	0.031900	0.002200	0.007100	0.003700	0.019300	0.005900	0.009700	0.007900	1.034000	0.000700	0.005700	0.020000	0.000300	32.263700	0.009300	168.799800	0.000000	0.006200	0.007200	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.001300	0.001400	0.000000	0.000000	0.000000	0.000000	0.000300	0.291400	1.102200	0.000000	0.003000	0.000000	21.010700	0.000300	0.767300	0.009400	0.001700	0.126900	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.019800	6.098000	1.301700	15.547100	10.401500	6.943100	8.651200	0.000000	0.011100	0.000000	0.561500	0.000000	0.010700	0.007300	0.006900	0.001600	0.505000	0.000000	0.461100	0.728000	0.051300	0.001200	0.396000	0.041600	0.003800	0.004100	82.323300	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.310000	0.111800	0.010800	0.013800	0.117100	0.003400	0.054900	0.091300	0.054900	0.180900	0.032800	0.022400	0.000000	0.000000	0.000000	2.788200	0.028300	0.984800	1.657400	0.008400	0.611400	0.000000	1.710100	2.234500	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.022400	0.537300	2.837200	0.028200	0.789900	5.215100	0.000000	0.000000	2.200100	0.013100	0.574100	0.000000	1.256500	2.056000	1.769400	1.017700	0.000000	0.010300	0.001000	0.002900	0.002000	0.005600	0.002600	0.004000	0.003800	0.379600	0.000300	0.001700	0.007600	0.000100	10.720400	0.002800	60.988200	0.000000	0.001700	0.002000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000400	0.000400	0.000000	0.000000	0.000000	0.000000	0.000100	0.087500	0.338300	0.000000	0.000800	0.000000	6.310100	0.000100	0.304600	0.003100	0.000500	0.034200	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.006200	2.054500	0.424000	2.737800	1.216300	0.734200	0.960900	0.000000	4.041600	0.000000	1.534000	0.000000	2.153100	0.000000	0.000000	0.441100	0.721700	0.000000	23.020000	0.486600	1.466600	0.363200	0.663700	1.119800	0.783700	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.156800	0.000000	14.120600	1.097300	0.351200	0.097400	0.216900	0.333600	0.308600	0.696800	0.084600	0.039900	0.000000	0.000000	0.000000	2.670900	0.903700	2.329400	0.694800	3.048900	1.442800	0.000000	0.991000	7.953400	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.716300	0.000000	2.600900	0.832500	2.402600	11.499700	0.000000	0.000000	1.101100	0.000000	1.687200	0.000000	0.645900	8.840600	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	13.722500	0.555800	4.888200	0.833000	0.034200	1.772000	4.813500	1.949600	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.028700	0.288000	0.467400	0.000000	0.312100	0.000000	2.681100	0.025800	1.310400	1.540000	0.170500	2.170000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.851600	0.614400	3.276100	0.105300	0.005100	0.001600	4.429400	0.444400	372.822000	71.038000	0.044600	6.110000	0.120000	0.018700	2.786000	0.052000	4.826900	1.496700	0.164600	0.891900	0.069900	0.017700	7.236900	242.286000	0.304900	0.970000	0.022400	0.412200	0.009100	0.370600	3.250400	317.196400	0.980200	3.540000	0.066700	1.039500	0.023000	0.663600	4.582000	-0.016900	0.003200	0.001000	0.000000	0.477800	0.006000	0.001700	1.197500	-0.016900	0.003200	0.001000	0.000000	-1.000000
25%	2966.260000	2452.247500	2181.044400	1081.875800	1.017700	100.000000	97.920000	0.121100	1.411200	-0.010800	-0.005600	0.958100	198.130700	0.000000	7.094875	406.127400	9.567625	0.968200	188.299825	12.460000	1.396500	-5933.250000	2578.000000	-4371.750000	-1476.000000	1.094800	1.906500	5.263700	67.377800	2.088900	0.161700	3.362700	84.490500	8.580000	50.252350	64.024800	49.421200	66.040650	86.578300	118.015600	74.800000	2.690000	70.000000	350.801575	9.925425	130.728875	724.442300	0.985000	136.926800	1.000000	625.928425	115.508975	0.000000	4.574000	4.816000	2836.000000	0.925450	0.946650	4.531900	-1.871575	350.596400	10.283000	112.022700	10.364300	17.364800	23.056425	698.770200	0.890700	145.237300	1.000000	612.774500	87.484200	145.305300	464.458100	0.000000	-0.019550	-0.051900	-0.029500	-0.047600	-0.010800	-0.044500	-0.027200	-0.018000	7.104225	0.129800	0.110725	2.376850	0.975800	1777.470300	0.169375	8564.689975	-0.042900	-0.001200	-0.001600	-0.000100	0.000000	-0.088600	0.000000	-0.218800	-0.029800	-0.000200	-0.000100	-0.035700	-0.011800	-0.000400	-0.001900	-0.001000	-0.048600	-0.064900	0.978800	100.389000	230.373800	0.459300	0.938600	0.000000	721.023000	0.989500	57.978300	0.594100	0.964800	6.246400	15.730000	3.202000	15.762500	15.722500	0.974400	2.572000	0.548900	3.074000	-0.898800	0.688700	0.996400	2.277300	999.996100	37.347250	92.000000	90.000000	81.300000	50.900100	243.786000	0.131700	0.000000	5.110000	0.003300	0.083900	0.048000	0.042300	0.010000	6.359900	0.000000	4.459250	8.089750	0.373750	0.007275	5.926950	0.240000	0.036250	0.027050	721.675050	411.000000	295.000000	1321.000000	451.000000	0.091000	0.068000	0.132000	2.100000	0.900000	0.090000	0.230000	0.575600	0.079800	0.276600	0.516800	0.276500	0.692200	0.196250	0.222000	0.000000	0.000000	16.850000	0.378000	7.732500	21.171500	0.102200	5.390000	0.000000	14.505000	24.711000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.218000	5.040000	17.130000	0.296000	6.740000	14.155000	5.020000	6.094000	24.653000	0.114300	6.040000	0.000000	16.350000	56.158000	0.000000	0.065600	0.043800	0.032500	0.036400	0.056800	0.063200	0.069550	0.045800	2.946100	0.002300	0.007800	0.040200	0.001400	95.147350	0.029775	718.725350	0.000000	0.013200	0.012600	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.003700	0.003600	0.000000	0.000000	0.000000	0.000000	0.001200	0.911500	2.725900	0.019200	0.014700	0.000000	76.132150	0.000700	2.205650	0.024500	0.004700	0.307600	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.044000	13.828000	2.956500	24.982300	30.013900	27.092725	18.247100	81.215600	0.044700	0.000000	1.697700	0.000900	0.028300	0.014200	0.011900	0.003300	2.210400	0.000000	1.438175	2.467200	0.114875	0.002400	2.092125	0.064900	0.012500	0.008725	229.809450	185.089800	130.220300	603.032900	210.936600	0.040700	0.030200	0.058900	0.717200	0.295800	0.030000	0.072800	0.225000	0.033100	0.113700	0.197600	0.113700	0.278550	0.077600	0.091500	0.000000	0.000000	0.000000	5.301525	0.117375	2.319725	6.245150	0.031200	1.670075	0.000000	4.272950	7.578600	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.068800	1.546550	5.453900	0.089400	2.035700	8.288525	1.542850	1.901350	7.588900	0.034600	1.911800	0.000000	4.998900	17.860900	4.440600	2.532700	0.000000	0.018000	0.019600	0.014600	0.016600	0.016000	0.030200	0.034850	0.021200	1.025475	0.000700	0.002200	0.013800	0.000400	32.168700	0.009500	228.682525	0.000000	0.003800	0.003500	0.002600	0.002200	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.001300	0.001300	0.000000	0.000000	0.000000	0.000000	0.000400	0.295500	0.842300	0.005300	0.004800	0.000000	24.386550	0.000200	0.675150	0.008300	0.001500	0.104400	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.014000	4.547600	0.966500	4.127800	3.012800	3.265075	2.321300	18.407900	11.375800	0.000000	4.927400	2.660100	5.765500	0.000000	0.000000	1.030400	3.184200	0.000000	55.976675	1.965250	3.766200	0.743425	3.113225	1.935500	2.571400	6.999700	11.059000	31.032400	10.027100	7.550700	3.494400	1.950900	3.070700	36.290300	48.173800	5.414100	0.679600	0.907650	0.550500	0.804800	0.555800	1.046800	0.226100	0.187700	0.000000	0.000000	0.000000	4.764200	3.747875	5.806525	2.899675	8.816575	3.827525	0.000000	2.291175	20.221850	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	4.697500	38.472775	4.847200	2.823300	5.807300	105.525150	24.900800	23.156500	3.494500	11.577100	4.105400	0.000000	2.627700	52.894500	0.000000	0.000000	81.316150	76.455400	50.383550	0.000000	55.555150	139.914350	112.859250	38.391100	1.747100	6.924650	1.663750	0.139000	5.274600	16.342300	8.150350	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	35.322200	0.000000	0.000000	0.000000	0.000000	0.000000	0.121500	0.890300	1.171200	4.160300	1.552150	0.000000	10.182800	0.073050	3.769650	4.101500	0.484200	4.895450	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.889900	1.385300	7.495750	0.109600	0.007800	0.002400	7.116000	0.797500	400.694000	73.254000	0.226250	14.530000	0.870000	0.094900	6.738100	0.343800	27.017600	3.625100	1.182900	0.955200	0.149825	0.036200	15.762450	259.972500	0.567100	4.980000	0.087700	2.090200	0.038200	1.884400	15.466200	530.702700	1.982900	7.500000	0.242250	2.567850	0.075100	1.408450	11.501550	0.013800	0.010600	0.003400	46.184900	0.497900	0.011600	0.003100	2.306500	0.013425	0.010600	0.003300	44.368600	-1.000000
50%	3011.490000	2499.405000	2201.066700	1285.214400	1.316800	100.000000	101.512200	0.122400	1.461600	-0.001300	0.000400	0.965800	199.535600	0.000000	8.967000	412.219100	9.851750	0.972600	189.664200	12.499600	1.406000	-5523.250000	2664.000000	-3820.750000	-78.750000	1.283000	1.986500	7.264700	69.155600	2.377800	0.186700	3.431000	85.135450	8.769800	50.396400	64.165800	49.603600	66.231800	86.820700	118.399300	78.290000	3.074000	70.000000	353.720900	10.034850	136.400000	733.450000	1.251050	140.007750	1.000000	631.370900	183.318150	0.000000	4.596000	4.843000	2854.000000	0.931000	0.949300	4.572700	0.947250	353.799100	10.436700	116.211800	13.246050	20.021350	26.261450	706.453600	0.978300	147.597300	1.000000	619.032700	102.604300	152.297200	466.081700	0.000000	-0.006300	-0.028900	-0.009900	-0.012500	0.000600	-0.008700	-0.019600	0.007600	7.467450	0.133000	0.113550	2.403900	0.987400	1809.249200	0.190100	8825.435100	0.000000	0.000400	-0.000200	0.000000	0.000000	0.003900	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	-0.010100	0.000000	-0.000200	0.000200	0.000000	-0.011200	0.981000	101.481700	231.201200	0.462850	0.946400	0.000000	750.861400	0.990500	58.549100	0.599000	0.969400	6.313600	15.790000	3.877000	15.830000	15.780000	1.144000	2.735000	0.653900	3.195000	-0.141900	0.758750	0.997750	2.312400	1004.050000	38.902600	109.000000	134.600000	117.700000	55.900100	339.561000	0.235800	0.000000	6.260000	0.003900	0.107500	0.058600	0.050000	0.015900	7.917300	0.000000	5.951000	10.993500	0.468700	0.011100	7.512700	0.320000	0.048700	0.035450	1020.300050	623.000000	438.000000	2614.000000	1784.000000	0.120000	0.089000	0.184000	2.600000	1.200000	0.119000	0.412000	0.686000	0.112500	0.323850	0.577600	0.323850	0.768200	0.242900	0.299000	0.000000	0.000000	18.690000	0.524000	10.170000	27.200500	0.132600	6.735000	0.000000	17.865000	40.209500	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.259000	6.780000	19.370000	0.424000	8.570000	17.235000	6.760000	8.462000	30.097000	0.158200	7.740000	0.000000	19.720000	73.248000	0.000000	0.079700	0.053200	0.041600	0.056000	0.075400	0.082500	0.084600	0.061700	3.630750	0.003000	0.008950	0.060900	0.002300	119.436000	0.039800	967.299800	0.000000	0.016500	0.015500	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.004600	0.004400	0.000000	0.000000	0.000000	0.000000	0.001700	1.185100	3.673000	0.027000	0.021000	0.000000	103.093600	0.001000	2.864600	0.030800	0.015000	0.405100	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.070600	17.977000	3.703500	28.773500	45.676500	40.019250	19.580900	110.601400	0.078400	0.000000	2.083100	0.001100	0.037200	0.016900	0.013900	0.005300	2.658000	0.000000	1.875150	3.360050	0.138950	0.003600	2.549000	0.083300	0.016900	0.011000	317.867100	278.671900	195.825600	1202.412100	820.098800	0.052800	0.040000	0.082800	0.860400	0.380800	0.038800	0.137200	0.264300	0.044800	0.129500	0.219450	0.129500	0.302900	0.097700	0.121500	0.000000	0.000000	0.000000	5.831500	0.163400	2.898900	8.388800	0.039850	2.077650	0.000000	5.458800	12.504500	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.084800	2.062700	5.980100	0.129400	2.513500	9.073550	2.054450	2.560850	9.474200	0.046400	2.377300	0.000000	6.005600	23.214700	5.567000	3.046400	0.000000	0.022600	0.024000	0.018800	0.025300	0.022000	0.042100	0.044200	0.029400	1.255300	0.000900	0.002400	0.019600	0.000700	39.696100	0.012500	309.831650	0.000000	0.004600	0.004300	0.003200	0.002800	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.001600	0.001500	0.000000	0.000000	0.000000	0.000000	0.000500	0.372600	1.106300	0.006800	0.006800	0.000000	32.530700	0.000300	0.877300	0.010200	0.004900	0.133900	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.023900	5.920100	1.239700	4.922450	4.489700	4.732750	2.548100	26.156900	20.255100	0.000000	6.176600	3.234000	7.395600	302.177600	272.448700	1.645100	3.943100	0.000000	69.905450	2.667100	4.764400	1.135300	3.941450	2.534100	3.453800	11.105600	16.381000	57.969300	151.115600	10.197700	4.551100	2.764300	3.780900	49.090900	65.437800	12.085900	0.807600	1.264550	0.643500	0.902700	0.651100	1.163800	0.279700	0.251200	0.000000	0.000000	0.000000	5.271450	5.227100	7.424900	3.724500	11.350900	4.793350	0.000000	2.830350	26.167850	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	5.645000	150.340100	5.472400	4.061100	7.396000	138.255150	34.246750	32.820050	4.276200	15.973800	5.242200	0.000000	3.184500	70.434500	0.000000	293.518500	148.317500	138.775500	112.953400	249.927000	112.275500	348.529400	219.487200	48.557450	2.250800	8.008950	2.529100	0.232500	6.607900	22.039100	10.906550	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	46.986100	0.000000	0.000000	0.000000	0.000000	0.000000	0.174700	1.154300	1.589100	5.832950	2.221000	0.000000	13.742600	0.100000	4.877100	5.134200	1.550100	6.410800	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.054800	1.785500	9.459300	0.109600	0.007800	0.002600	7.116000	0.911100	403.122000	74.084000	0.471000	16.340000	1.150000	0.197900	7.427900	0.478900	54.441700	4.067100	1.529800	0.972700	0.290900	0.059200	29.731150	264.272000	0.651000	5.160000	0.119550	2.150450	0.048650	1.999700	16.988350	532.398200	2.118600	8.650000	0.293400	2.975800	0.089500	1.624500	13.817900	0.020400	0.014800	0.004700	72.288900	0.500200	0.013800	0.003600	2.757650	0.020500	0.014800	0.004600	71.900500	-1.000000
75%	3056.650000	2538.822500	2218.055500	1591.223500	1.525700	100.000000	104.586700	0.123800	1.516900	0.008400	0.005900	0.971300	202.007100	0.000000	10.861875	419.089275	10.128175	0.976800	192.189375	12.547100	1.415000	-5356.250000	2841.750000	-3352.750000	1377.250000	1.304300	2.003200	7.329700	72.266700	2.655600	0.207100	3.531300	85.741900	9.060600	50.578800	64.344700	49.747650	66.343275	87.002400	118.939600	80.200000	3.521000	70.000000	360.772250	10.152475	142.098225	741.454500	1.340350	143.195700	1.000000	638.136325	206.977150	0.000000	4.617000	4.869000	2874.000000	0.933100	0.952000	4.668600	4.385225	359.673600	10.591600	120.927300	16.376100	22.813625	29.914950	714.597000	1.065000	149.959100	1.000000	625.170000	115.498900	158.437800	467.889900	0.000000	0.007100	-0.006500	0.009250	0.012200	0.013200	0.009100	-0.012000	0.026900	7.807625	0.136300	0.114900	2.428600	0.989700	1841.873000	0.200425	9065.432400	0.050700	0.002000	0.001000	0.000100	0.000100	0.122000	0.000000	0.189300	0.029800	0.000200	0.000100	0.033600	-0.008200	0.000400	0.001100	0.001600	0.049000	0.038000	0.982300	102.078100	233.036100	0.466425	0.952300	0.000000	776.781850	0.990900	59.133900	0.603400	0.978300	6.375850	15.860000	4.392000	15.900000	15.870000	1.338000	2.873000	0.713500	3.311000	0.047300	0.814500	0.998900	2.358300	1008.670600	40.804600	127.000000	181.000000	161.600000	62.900100	502.205900	0.439100	0.000000	7.500000	0.004900	0.132700	0.071800	0.061500	0.021300	9.585300	0.000000	8.275000	14.347250	0.679925	0.014900	9.054675	0.450000	0.066700	0.048875	1277.750125	966.000000	625.000000	5034.000000	6384.000000	0.154000	0.116000	0.255000	3.200000	1.500000	0.151000	0.536000	0.797300	0.140300	0.370200	0.634500	0.370200	0.843900	0.293925	0.423000	0.000000	0.000000	20.972500	0.688750	13.337500	31.687000	0.169150	8.450000	0.000000	20.860000	57.674750	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.296000	9.555000	21.460000	0.726000	11.460000	20.162500	9.490000	11.953000	33.506000	0.230700	9.940000	0.000000	22.370000	90.515000	0.000000	0.099450	0.064200	0.062450	0.073700	0.093550	0.098300	0.097550	0.086350	4.404750	0.003800	0.010300	0.076500	0.005500	144.502800	0.061300	1261.299800	0.000000	0.021200	0.020000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.005700	0.005300	0.000000	0.000000	0.000000	0.000000	0.002600	1.761800	4.479700	0.051500	0.027300	0.000000	131.758400	0.001300	3.795050	0.037900	0.021300	0.480950	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.091650	24.653000	4.379400	31.702200	59.594700	54.277325	22.097300	162.038200	0.144900	0.000000	2.514300	0.001300	0.045800	0.020700	0.016600	0.007100	3.146200	0.000000	2.606950	4.311425	0.198450	0.004900	3.024525	0.118100	0.023600	0.014925	403.989300	428.554500	273.952600	2341.288700	3190.616400	0.069200	0.052000	0.115500	1.046400	0.477000	0.048600	0.178500	0.307500	0.055200	0.147600	0.237900	0.147600	0.331900	0.115900	0.160175	0.000000	0.000000	0.000000	6.547800	0.218100	4.021250	9.481100	0.050200	2.633350	0.000000	6.344875	17.925175	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.095600	2.790525	6.549500	0.210400	3.360400	10.041625	2.785475	3.405450	10.439900	0.066800	2.985400	0.000000	6.885200	28.873100	6.825500	4.085700	0.000000	0.027300	0.028600	0.028500	0.033900	0.026900	0.050200	0.050000	0.042300	1.533325	0.001100	0.002700	0.025000	0.001800	47.079200	0.018600	412.329775	0.000000	0.005800	0.005400	0.004200	0.003600	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.001900	0.001800	0.000000	0.000000	0.000000	0.000000	0.000800	0.541200	1.386600	0.011325	0.009300	0.000000	42.652450	0.000400	1.148200	0.012400	0.006900	0.160400	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.032300	8.585200	1.416700	5.787100	5.936700	6.458300	2.853200	38.139700	29.307300	0.000000	7.570700	4.010700	9.168800	524.002200	582.935200	2.214700	4.784300	0.000000	92.911500	3.470975	6.883500	1.539500	4.768650	3.609000	4.755800	17.423100	21.765200	120.172900	305.026300	12.754200	5.822800	3.822200	4.678600	66.666700	84.973400	15.796400	0.927600	1.577825	0.733425	0.988800	0.748400	1.272300	0.338825	0.351100	0.000000	0.000000	0.000000	5.913000	6.902475	9.576775	4.341925	14.387900	6.089450	0.000000	3.309225	35.278800	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	6.386900	335.922400	6.005700	7.006800	9.720200	168.410125	47.727850	45.169475	4.741800	23.737200	6.703800	0.000000	3.625300	93.119600	0.000000	514.585900	262.865250	294.667050	288.893450	501.607450	397.506100	510.647150	377.144200	61.494725	2.839800	9.078900	3.199100	0.563000	7.897200	32.438475	14.469050	0.000000	536.204600	505.401000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	64.248700	555.294100	0.000000	0.000000	0.000000	0.000000	0.264900	1.759700	1.932800	10.971850	2.903700	0.000000	17.808950	0.133200	6.450650	6.329500	2.211650	7.594250	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.947000	2.458350	11.238400	0.113400	0.009000	0.002600	8.020700	1.285550	407.431000	78.397000	0.850350	19.035000	1.370000	0.358450	8.637150	0.562350	74.628700	4.702700	1.815600	1.000800	0.443600	0.089000	44.113400	265.707000	0.768875	7.800000	0.186150	3.098725	0.075275	2.970850	24.772175	534.356400	2.290650	10.130000	0.366900	3.492500	0.112150	1.902000	17.080900	0.027700	0.020000	0.006475	116.539150	0.502375	0.016500	0.004100	3.295175	0.027600	0.020300	0.006400	114.749700	-1.000000
max	3356.350000	2846.440000	2315.266700	3715.041700	1114.536600	100.000000	129.252200	0.128600	1.656400	0.074900	0.053000	0.984800	272.045100	0.000000	19.546500	824.927100	102.867700	0.984800	215.597700	12.989800	1.453400	0.000000	3656.250000	2363.000000	14106.000000	1.382800	2.052800	7.658800	77.900000	3.511100	0.285100	4.804400	105.603800	23.345300	59.771100	94.264100	50.165200	67.958600	88.418800	133.389800	86.120000	37.880000	70.000000	377.297300	11.053000	176.313600	789.752300	1.511100	163.250900	1.000000	667.741800	258.543200	0.000000	4.764000	5.011000	2936.000000	0.937800	0.959800	4.847500	168.145500	373.866400	11.784900	287.150900	188.092300	48.988200	118.083600	770.608400	7272.828300	167.830900	1.000000	722.601800	238.477500	175.413200	692.425600	4.195500	0.231500	0.072300	0.133100	0.249200	0.101300	0.118600	0.058400	0.143700	8.990400	0.150500	0.118400	2.555500	0.993500	2105.182300	1.472700	10746.600000	0.362700	0.028100	0.013300	0.001100	0.000900	2.509300	0.000000	2.569800	0.885400	0.002300	0.001700	0.297900	0.020300	0.007100	0.012700	0.017200	0.485600	0.393800	0.984200	106.922700	236.954600	0.488500	0.976300	0.041400	924.531800	0.992400	311.734400	0.624500	0.982700	7.522000	16.070000	6.889000	16.100000	16.100000	2.465000	3.991000	1.175000	3.895000	2.458000	0.888400	1.019000	2.472300	1020.994400	64.128700	994.000000	295.800000	334.700000	141.799800	1770.690900	9998.894400	0.000000	103.390000	0.012100	0.625300	0.250700	0.247900	0.978300	742.942100	0.000000	22.318000	536.564000	924.378000	0.238900	191.547800	12.710000	2.201600	0.287600	2505.299800	7791.000000	4170.000000	37943.000000	36871.000000	0.957000	1.817000	3.286000	21.100000	16.300000	0.725000	1.143000	1.153000	0.494000	0.548400	0.864300	0.548400	1.172000	0.441100	1.858000	0.000000	0.000000	48.670000	3.573000	55.000000	72.947000	3.228300	267.910000	0.000000	307.930000	191.830000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	4.838000	396.110000	252.870000	10.017000	390.120000	199.620000	126.530000	490.561000	500.349000	9998.448300	320.050000	2.000000	457.650000	172.349000	46.150000	0.516400	0.322700	0.594100	1.283700	0.761500	0.342900	0.282800	0.674400	8.801500	0.016300	0.024000	0.230500	0.991100	1768.880200	1.436100	3601.299800	0.000000	0.154100	0.213300	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.024400	0.023600	0.000000	0.000000	0.000000	0.000000	1.984400	99.902200	237.183700	0.491400	0.973200	0.413800	1119.704200	0.990900	2549.988500	0.451700	0.078700	0.925500	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.157800	40.855000	10.152900	158.526000	132.647900	122.117400	43.573700	659.169600	3332.596400	0.000000	32.170900	0.003400	0.188400	0.075500	0.059700	0.308300	232.804900	0.000000	6.869800	207.016100	292.227400	0.074900	59.518700	4.420300	0.691500	0.083100	879.226000	3933.755000	2005.874400	15559.952500	18520.468300	0.526400	1.031200	1.812300	5.711000	5.154900	0.225800	0.333700	0.475000	0.224600	0.211200	0.323900	0.211200	0.443800	0.178400	0.754900	0.000000	0.000000	0.000000	13.095800	1.003400	15.893400	20.045500	0.947400	79.151500	0.000000	89.191700	51.867800	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.095900	174.894400	90.515900	3.412500	172.711900	214.862800	38.899500	196.688000	197.498800	5043.878900	97.708900	0.447200	156.336000	59.324100	257.010600	187.758900	13.914700	0.220000	0.133900	0.291400	0.618800	0.142900	0.153500	0.134400	0.278900	2.834800	0.005200	0.004700	0.088800	0.409000	547.172200	0.416300	1072.203100	0.000000	0.036800	0.039200	0.035700	0.033400	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.008200	0.007700	0.000000	0.000000	0.000000	0.000000	0.627100	30.998200	74.844500	0.207300	0.306800	0.130900	348.829300	0.312700	805.393600	0.137500	0.022900	0.299400	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.051400	14.727700	3.312800	44.310000	9.576500	13.807100	6.215000	128.281600	899.119000	0.000000	116.861500	9.690000	39.037600	999.316000	998.681300	111.495600	273.095200	0.000000	424.215200	103.180900	898.608500	24.990400	113.223000	118.753300	186.616400	400.000000	400.000000	994.285700	995.744700	400.000000	400.000000	400.000000	32.274000	851.612900	657.762100	33.058000	1.277100	5.131700	1.085100	1.351100	1.108700	1.763900	0.508500	1.475400	0.000000	0.000000	0.000000	13.977600	34.490200	42.070300	10.184000	232.125800	164.109300	0.000000	47.777200	149.385100	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	109.007400	999.877000	77.800700	87.134700	212.655700	492.771800	358.950400	415.435500	79.116200	274.887100	289.826400	200.000000	63.333600	221.974700	0.000000	999.413500	989.473700	996.858600	994.000000	999.491100	995.744700	997.518600	994.003500	142.843600	12.769800	21.044300	9.402400	127.572800	107.692600	219.643600	40.281800	0.000000	1000.000000	999.233700	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	451.485100	1000.000000	0.000000	0.000000	0.000000	0.000000	252.860400	113.275800	111.349500	184.348800	111.736500	1000.000000	137.983800	111.333000	818.000500	80.040600	8.203700	14.447900	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	6.580300	4.082500	25.779200	0.118400	0.024000	0.004700	21.044300	3.978600	421.702000	83.720000	7.065600	131.680000	39.330000	2.718200	56.930300	17.478100	303.550000	35.319800	54.291700	1.512100	1.073700	0.445700	101.114600	311.404000	1.298800	32.580000	0.689200	14.014100	0.293200	12.746200	84.802400	589.508200	2.739500	454.560000	2.196700	170.020400	0.550200	90.423500	96.960100	0.102800	0.079900	0.028600	737.304800	0.509800	0.476600	0.104500	99.303200	0.102800	0.079900	0.028600	737.304800	1.000000

	0	1	2	3	4	6	12	14	15	16	...	568	569	570	572	574	576	577	585	589	Pass/Fail
0	3030.93	2564.00	2187.7333	1411.1265	1.3602	97.6133	202.4396	7.9558	414.8710	10.0433	...	2.452417	21.117674	533.8500	8.95	3.0624	1.6765	14.9509	2.3630	99.670066	-1
1	3095.78	2465.14	2230.4222	1463.6606	0.8294	102.3433	200.5470	10.1548	414.7347	9.2599	...	2.452417	21.117674	535.0164	5.92	2.0111	1.1065	10.9003	4.4447	208.204500	-1
2	2932.61	2559.94	2186.4111	1698.0172	1.5102	95.4878	202.0179	9.5157	416.7075	9.3144	...	0.411900	68.848900	535.0245	11.21	4.0923	2.0952	9.2721	3.1745	82.860200	1
3	2988.72	2479.90	2199.0333	909.7926	1.3204	104.2367	201.8482	9.6052	422.2894	9.6924	...	2.729000	25.036300	530.5682	9.33	2.8971	1.7585	8.5831	2.0544	73.843200	-1
4	3032.24	2502.87	2233.3667	1326.5200	1.5334	100.3967	201.9424	10.5661	420.5925	10.3387	...	2.452417	21.117674	532.0155	8.83	3.1776	1.6597	10.9698	99.3032	73.843200	-1
5	2946.25	2432.84	2233.3667	1326.5200	1.5334	100.3967	200.4720	8.6617	414.2426	9.2441	...	1.870000	22.559800	534.2091	8.91	2.2598	1.6679	13.7755	3.8276	44.007700	-1
6	3030.27	2430.12	2230.4222	1463.6606	0.8294	102.3433	202.0901	9.0350	415.8852	9.9990	...	2.452417	21.117674	541.9036	6.48	2.2019	1.1958	8.3645	2.8515	44.007700	-1
7	3058.88	2690.15	2248.9000	1004.4692	0.7884	106.2400	202.4170	13.6872	408.4017	9.6836	...	2.934900	23.605200	493.0054	278.19	92.5866	56.4274	16.0862	2.1261	95.031000	-1
8	2967.68	2600.47	2248.9000	1004.4692	0.7884	106.2400	202.4544	12.6837	417.6009	9.7046	...	2.368200	18.212000	535.1818	7.09	2.4902	1.3248	14.2892	3.4456	111.652500	-1
9	3016.11	2428.37	2248.9000	1004.4692	0.7884	106.2400	202.5999	12.4278	413.3677	9.7046	...	2.654500	5.861700	533.4200	3.54	1.0395	0.6636	7.4181	3.0687	90.229400	-1

	0	1	2	3	4	6	12	14	15	16	...	568	569	570	572	574	576	577	585	589	Pass/Fail
0	1.000000	-0.143840	0.004756	-0.007613	-0.011014	0.002270	0.010368	-0.007058	0.030675	-0.005749	...	0.060010	0.049862	-0.018953	0.013678	0.015206	0.013228	0.008601	0.023589	0.004174	-0.025141
1	-0.143840	1.000000	0.005767	-0.007568	-0.001636	-0.025564	0.034062	-0.037667	-0.087315	-0.001878	...	-0.017051	-0.025490	-0.009000	0.001753	0.001303	0.002570	-0.010145	0.002273	0.044797	-0.002603
2	0.004756	0.005767	1.000000	0.298935	0.095891	-0.136225	0.018326	0.006476	0.006115	-0.000788	...	0.050434	0.064282	-0.037070	-0.000518	0.001342	0.002592	-0.028705	0.015752	-0.032890	-0.000957
3	-0.007613	-0.007568	0.298935	1.000000	-0.058483	-0.685835	-0.028223	-0.019827	-0.013157	-0.004596	...	0.008646	0.046434	0.002231	0.007634	0.006822	0.008216	0.016438	0.026019	-0.080341	-0.024623
4	-0.011014	-0.001636	0.095891	-0.058483	1.000000	-0.074368	-0.002707	-0.017523	0.011435	-0.001763	...	-0.012944	0.027696	0.005273	-0.012024	-0.012264	-0.012163	-0.004070	-0.001616	0.050910	-0.013756
6	0.002270	-0.025564	-0.136225	-0.685835	-0.074368	1.000000	0.058982	0.055333	0.039815	0.040015	...	0.016000	-0.070722	0.017264	0.009292	0.007783	0.007409	-0.012342	-0.039517	0.043777	0.016239
12	0.010368	0.034062	0.018326	-0.028223	-0.002707	0.058982	1.000000	-0.012805	-0.033933	0.552542	...	0.003572	-0.007246	-0.000639	0.036757	0.032908	0.035743	0.031434	0.000523	-0.036720	-0.005969
14	-0.007058	-0.037667	0.006476	-0.019827	-0.017523	0.055333	-0.012805	1.000000	0.133463	-0.015420	...	-0.008181	0.002705	0.020110	-0.000807	0.000409	-0.000985	0.009505	0.002535	0.068161	-0.068975
15	0.030675	-0.087315	0.006115	-0.013157	0.011435	0.039815	-0.033933	0.133463	1.000000	-0.020408	...	0.014993	0.000864	0.011928	-0.024789	-0.024032	-0.023509	-0.019152	0.017745	0.009764	-0.002884
16	-0.005749	-0.001878	-0.000788	-0.004596	-0.001763	0.040015	0.552542	-0.015420	-0.020408	1.000000	...	-0.007759	-0.009876	0.007679	-0.014068	-0.014005	-0.014167	-0.004396	0.002643	-0.013918	0.002356

	0	1	2	3	4	6	12	14	15	16	...	568	569	570	572	574	576	577	585	589	Pass/Fail
0	NaN	-0.14384	0.004756	-0.007613	-0.011014	0.002270	0.010368	-0.007058	0.030675	-0.005749	...	0.060010	0.049862	-0.018953	0.013678	0.015206	0.013228	0.008601	0.023589	0.004174	-0.025141
1	NaN	NaN	0.005767	-0.007568	-0.001636	-0.025564	0.034062	-0.037667	-0.087315	-0.001878	...	-0.017051	-0.025490	-0.009000	0.001753	0.001303	0.002570	-0.010145	0.002273	0.044797	-0.002603
2	NaN	NaN	NaN	0.298935	0.095891	-0.136225	0.018326	0.006476	0.006115	-0.000788	...	0.050434	0.064282	-0.037070	-0.000518	0.001342	0.002592	-0.028705	0.015752	-0.032890	-0.000957
3	NaN	NaN	NaN	NaN	-0.058483	-0.685835	-0.028223	-0.019827	-0.013157	-0.004596	...	0.008646	0.046434	0.002231	0.007634	0.006822	0.008216	0.016438	0.026019	-0.080341	-0.024623
4	NaN	NaN	NaN	NaN	NaN	-0.074368	-0.002707	-0.017523	0.011435	-0.001763	...	-0.012944	0.027696	0.005273	-0.012024	-0.012264	-0.012163	-0.004070	-0.001616	0.050910	-0.013756
6	NaN	NaN	NaN	NaN	NaN	NaN	0.058982	0.055333	0.039815	0.040015	...	0.016000	-0.070722	0.017264	0.009292	0.007783	0.007409	-0.012342	-0.039517	0.043777	0.016239
12	NaN	NaN	NaN	NaN	NaN	NaN	NaN	-0.012805	-0.033933	0.552542	...	0.003572	-0.007246	-0.000639	0.036757	0.032908	0.035743	0.031434	0.000523	-0.036720	-0.005969
14	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.133463	-0.015420	...	-0.008181	0.002705	0.020110	-0.000807	0.000409	-0.000985	0.009505	0.002535	0.068161	-0.068975
15	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	-0.020408	...	0.014993	0.000864	0.011928	-0.024789	-0.024032	-0.023509	-0.019152	0.017745	0.009764	-0.002884
16	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	-0.007759	-0.009876	0.007679	-0.014068	-0.014005	-0.014167	-0.004396	0.002643	-0.013918	0.002356

	mean_fit_time	std_fit_time	mean_score_time	std_score_time	param_criterion	param_max_depth	param_min_samples_leaf	params	split0_test_score	split1_test_score	split2_test_score	split3_test_score	split4_test_score	mean_test_score	std_test_score	rank_test_score
0	0.023294	0.004027	0.000929	0.000824	gini	1	2	{'criterion': 'gini', 'max_depth': 1, 'min_sam...	0.663943	0.737148	0.723982	0.708175	0.678060	0.702262	0.027488	289
1	0.025248	0.005607	0.000000	0.000000	gini	1	3	{'criterion': 'gini', 'max_depth': 1, 'min_sam...	0.663943	0.737148	0.723982	0.708175	0.678060	0.702262	0.027488	289
2	0.021961	0.007750	0.000000	0.000000	gini	1	4	{'criterion': 'gini', 'max_depth': 1, 'min_sam...	0.663943	0.737148	0.723982	0.708175	0.678060	0.702262	0.027488	289
3	0.021959	0.009410	0.003125	0.006249	gini	1	5	{'criterion': 'gini', 'max_depth': 1, 'min_sam...	0.663943	0.737148	0.723982	0.708175	0.678060	0.702262	0.027488	289
4	0.025113	0.007746	0.000000	0.000000	gini	1	6	{'criterion': 'gini', 'max_depth': 1, 'min_sam...	0.663943	0.737148	0.723982	0.708175	0.678060	0.702262	0.027488	289
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
299	0.217685	0.023812	0.000000	0.000000	entropy	19	5	{'criterion': 'entropy', 'max_depth': 19, 'min...	0.847688	0.845557	0.859899	0.829038	0.876242	0.851685	0.015729	52
300	0.212908	0.019606	0.000000	0.000000	entropy	19	6	{'criterion': 'entropy', 'max_depth': 19, 'min...	0.851819	0.817694	0.866725	0.836268	0.847534	0.844008	0.016382	86
301	0.210777	0.020718	0.000000	0.000000	entropy	19	7	{'criterion': 'entropy', 'max_depth': 19, 'min...	0.829675	0.808758	0.852076	0.817474	0.840259	0.829648	0.015493	147
302	0.210576	0.021159	0.000000	0.000000	entropy	19	8	{'criterion': 'entropy', 'max_depth': 19, 'min...	0.823256	0.814581	0.847751	0.812500	0.834106	0.826439	0.013100	178
303	0.207728	0.017968	0.000000	0.000000	entropy	19	9	{'criterion': 'entropy', 'max_depth': 19, 'min...	0.821791	0.811044	0.831926	0.830292	0.840415	0.827094	0.009967	168

	mean_fit_time	std_fit_time	mean_score_time	std_score_time	param_min_samples_leaf	param_max_depth	param_criterion	params	split0_test_score	split1_test_score	split2_test_score	split3_test_score	split4_test_score	mean_test_score	std_test_score	rank_test_score
0	0.190554	0.015333	0.003526	0.006100	3	19	gini	{'min_samples_leaf': 3, 'max_depth': 19, 'crit...	0.831075	0.873154	0.834760	0.867347	0.887764	0.858820	0.022202	1
1	0.219806	0.013554	0.000000	0.000000	3	11	entropy	{'min_samples_leaf': 3, 'max_depth': 11, 'crit...	0.839895	0.828500	0.845960	0.830565	0.881057	0.845195	0.019013	5
2	0.044263	0.006368	0.000000	0.000000	7	2	gini	{'min_samples_leaf': 7, 'max_depth': 2, 'crite...	0.742138	0.737483	0.715241	0.763221	0.728430	0.737303	0.015879	9
3	0.161549	0.007537	0.000000	0.000000	5	11	gini	{'min_samples_leaf': 5, 'max_depth': 11, 'crit...	0.802357	0.871314	0.820578	0.852473	0.873965	0.844138	0.028278	6
4	0.179896	0.018663	0.005418	0.006644	4	19	gini	{'min_samples_leaf': 4, 'max_depth': 19, 'crit...	0.818438	0.875811	0.836364	0.866725	0.875357	0.854539	0.023100	3
5	0.160784	0.014280	0.000000	0.000000	7	13	gini	{'min_samples_leaf': 7, 'max_depth': 13, 'crit...	0.784225	0.849492	0.812274	0.840444	0.847382	0.826763	0.025093	7
6	0.233045	0.034323	0.002803	0.005606	2	17	entropy	{'min_samples_leaf': 2, 'max_depth': 17, 'crit...	0.860692	0.852614	0.850340	0.827815	0.885276	0.855347	0.018520	2
7	0.041059	0.008720	0.000000	0.000000	5	2	gini	{'min_samples_leaf': 5, 'max_depth': 2, 'crite...	0.742138	0.737483	0.715241	0.763221	0.728430	0.737303	0.015879	9
8	0.217011	0.015517	0.000000	0.000000	5	18	entropy	{'min_samples_leaf': 5, 'max_depth': 18, 'crit...	0.847688	0.845557	0.859899	0.829038	0.876242	0.851685	0.015729	4
9	0.097550	0.006238	0.000000	0.000000	5	5	gini	{'min_samples_leaf': 5, 'max_depth': 5, 'crite...	0.763828	0.847234	0.805085	0.791536	0.802161	0.801969	0.026916	8

	Method	Train Accuracy	Test Accuracy	Recall	Precision	F1-Score
0	Decision Tree	0.979490	87.431694	92.622951	83.910891	88.051948
1	Logistic Regression	0.875114	82.377049	86.065574	80.152672	83.003953

	Method	Train Accuracy	Test Accuracy	Recall	Precision	F1-Score
0	Decision Tree	0.97949	87.431694	92.622951	83.910891	88.051948
1	Logistic Regression	0.875114	82.377049	86.065574	80.152672	83.003953
2	KNN	0.93	66.803279	100.000000	60.098522	75.076923

FEATURISATION & MODEL TUNING¶

• DOMAIN: Semiconductor manufacturing process¶

• DATA DESCRIPTION: signal-data.csv : (1567, 592)¶

• PROJECT OBJECTIVE: We will build a classifier to predict the Pass/Fail yield of a particular process entity and analyse whether all the features are required to build the model or not.¶

Steps and tasks:¶

1. Import and understand the data.¶

1.A. Import ‘signal-data.csv’ as DataFrame.¶

1.B. Print 5 point summary and share at least 2 observations.¶

Observations¶

2. Data cleansing:¶

2.A. Write a for loop which will remove all the features with 20%+ Null values and impute rest with mean of the feature¶

Observations¶

2.B. Identify and drop the features which are having same value for all the rows.¶

2.C. Drop other features if required using relevant functional knowledge. Clearly justify the same¶

Observations¶

2.D. Check for multi-collinearity in the data and take necessary action¶

Observations¶

2.E. Make all relevant modifications on the data using both functional/logical reasoning/assumptions¶

3. Data analysis & visualisation¶

3.A. Perform a detailed univariate Analysis with appropriate detailed comments after each analysis.¶

Univariate analysis of all the predictors feature¶

Univariate analysis of all the Target Variable¶

Observations¶

Outlier analysis using boxplot¶

Observations:¶

3.B. Perform bivariate and multivariate analysis with appropriate detailed comments after each analysis.¶

Observations¶

4. Data pre-processing¶

4.A. Segregate predictors vs target attributes.¶

4.B. Check for target balancing and fix it if found imbalanced.¶

Observation:¶

Observation:¶

4.C. Perform train-test split and standardise the data or vice versa if required.¶

4.D. Check if the train and test data have similar statistical characteristics when compared with original data.¶

Observation:¶

5. Model training, testing and tuning¶

5.A. Use any Supervised Learning technique to train a model¶

5.B. Use cross validation techniques.¶

5.C. Apply hyper-parameter tuning techniques to get the best accuracy.¶

Observation:¶

5.D. Use any other technique/method which can enhance the model performance.¶

Observation:¶

5.E. Display and explain the classification report in detail.¶

5.F. Apply the above steps for all possible models that you have learnt so far.¶

Model 2 - Logistic Regression¶

6. Post Training and Conclusion:¶

6.A. Display and compare all the models designed with their train and test accuracies.¶

6.B. Select the final best trained model along with your detailed comments for selecting this model.¶

Observations:¶

6.C.Pickle the selected model for future use¶

6.D. Write your conclusion on the results¶

Observation:¶